Over a life course, human adaptive immunity to antigenically mutable pathogens exhibits competitive and facilitative interactions. We hypothesize that such interactions may lead to cyclic dynamics in immune responses over a lifetime.To investigate the cyclic behavior, we analyzed hemagglutination inhibition titers against 21 historical influenza A(H3N2) strains spanning 47 years from a cohort in Guangzhou, China, and applied Fourier spectrum analysis. To investigate possible biological mechanisms, we simulated individual antibody profiles encompassing known feedbacks and interactions due to generally recognized immunological mechanisms.We demonstrated a long-term periodicity (about 24 years) in individual antibody responses. The reported cycles were robust to analytic and sampling approaches. Simulations suggested that individual-level cross-reaction between antigenically similar strains likely explains the reported cycle. We showed that the reported cycles are predictable at both individual and birth cohort level and that cohorts show a diversity of phases of these cycles. Phase of cycle was associated with the risk of seroconversion to circulating strains, after accounting for age and pre-existing titers of the circulating strains.Our findings reveal the existence of long-term periodicities in individual antibody responses to A(H3N2). We hypothesize that these cycles are driven by preexisting antibody responses blunting responses to antigenically similar pathogens (by preventing infection and/or robust antibody responses upon infection), leading to reductions in antigen-specific responses over time until individual’s increasing risk leads to an infection with an antigenically distant enough virus to generate a robust immune response. These findings could help disentangle cohort effects from individual-level exposure histories, improve our understanding of observed heterogeneous antibody responses to immunizations, and inform targeted vaccine strategy.This study was supported by grants from the NIH R56AG048075 , NIH R01AI114703 , the Wellcome Trust 200861/Z/16/Z (SR), and 200187/Z/15/Z (SR). This work was also supported by research grants from Guangdong Government HZQB-KCZYZ-2021014 and 2019B121205009 (YG and HZ). DATC, JMR and SR acknowledge support from the National Institutes of Health Fogarty Institute (R01TW0008246). JMR acknowledges support from the Medical Research Council (MR/S004793/1) and the Engineering and Physical Sciences Research Council (EP/N014499/1). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. Over a life course, a key feature of human adaptive immune responses is the ability to continually update and refine responses to new antigens. A key example is immune responses to influenza, a pathogen that is constantly experiencing genetic and antigenic change. Antibodies mounted against a specific influenza virus decay (in either absolute magnitude or antigenic relevance) after exposure until re-exposure or infection to an antigenically similar virus occurs, whereupon back-boosting of antibodies acquired from previous infections  can occur, as well as updating antigen-specific antibodies to the newly encountered infection  . As antiOriginal antigenic sin (OAS) is a widely accepted concept describing the hierarchical and persistent memory of antibodies from the primary exposure to a pathogen in childhood. Recent studies suggested that non-neutralizing antibodies acquired from previous exposures can be boosted and may blunt the immune responses to new influenza infections  . AntibodHere, we examine seasonal influenza as a case study. Three subtypes of influenza (A(H3N2), A(H1N1), and B) cause an estimated 291,000–645,000 deaths globally every year . AlthougTo test the hypothesis, we characterized the periodic behavior of 777 paired antibody profiles, measured in 2010 (baseline) and 2014 (follow-up), measuring antibody responses  to 21 AH3N2) strains circulating over 47 years N2 strain. Only 0.Antibody titers against a set of strains isolated over 47 years, when ordered by the time of isolation of the tested strains, form a time series that describes the immune history of an individual and cover a range of antigenic distances . To descWe investigated whether cyclic behavior was present in antibody responses by performing Fourier analysis on each individual’s time series of residuals . The perWe found that 33.6%  of participants had a peak frequency corresponding to a long-term periodicity  at baseline, which was significantly higher than null expectation , suggestWe conducted multiple sensitivity analyses and validations to test the robustness of the observed cycles in individual-level antibody responses to analytic methods and our sampling methods. Across these analyses, including methods that accounted for variation in each individual’s spectra , irregulAdditionally, we analyzed an independent out-of-sample data set from Vietnam . We tested several potential biological mechanisms that can shape individual antibody profiles through influencing individual infection hazard  and antibody responses after exposures  . The breWe assessed the periodic pattern of the simulation from two perspectives. First, we compared whether the peak frequency distribution from the simulation was significantly different from the null distribution to determine whether the simulated antibody profiles were periodic . Next, wMultiple models showed qualitatively similar periodic behavior to data that is different from null distribution and had a significantly higher proportion of simulated individual responses with long-term periodicity . A key mThese results suggested that, after accounting for the impact of population-level A(H3N2) circulation, cross-reactivity from previously infected strains likely explained the reported cyclic patterns in an individual’s antibody responses. As such, we hypothesized (1) that the position of individuals in their antibody response cycles could be predicted years in advance if the periodic behavior was stable over 3–4 years and (2) that the position of individuals in their antibody response cycles are associated with responses to future strains. We measured the position in antibody response cycles using phase angles .To test the first hypothesis, we predicted the phase of individual antibody response residuals to strains circulating in 2012  between baseline and follow-up on the above-mentioned predicted phase in 2012 (using strains isolated between 1968 and 2002), and adjusted for biological age at baseline and the average preexisting log-titer of the two strains. We found that individuals who were predicted to be in phase IV  were 14%  more likely to experience seroconversion to the two recent strains compared to those in phase I .As a result of resonance, we expected intrinsic cycles in individual antibody responses to be correlated across birth cohorts . There wWe demonstrate that human antibody responses to influenza A(H3N2) display long-term periodicity, which are biologically consistent with nonlinear human adaptive immune responses  to evolving viruses. Our observations are validated by different analytic methods and validation in a separate study population tested by a different antibody assay. Our findings were robust to our sampling and interpolation methods. We further demonstrate that, at both individual and birth cohort levels, the phases of the antibody responses to the currently circulating strains are predictable and associated with seroconversion to these strains independent from the preexisting titers and age. Such findings could improve our forecasting of the individual and birth cohort-level risks of infections and our understanding of heterogenous immune responses and vaccine effectiveness against influenza viruses.We were able to qualitatively recover the observed long-term periodicity only when including cross-reactions between antigenically similar strains in the simulations. Particularly, our simulation results suggested that model including repeated exposures or population-level A(H3N2) activity alone did not recover the long-term periodicity . Such fiWithin-subtype cross-reactions may drive individual-level long-term periodicity in antibody responses through temporal (but waning) cross-protection  and blunting generation of specific antibodies  against the circulating strains. A recent cohort study found that homotypic cross-protection against PCR-confirmed infections for up to five seasons after infections supported cross-protection that eventually wanes . In addiOur findings suggest that long-term periodicity in HI titer may be driven by broad cross-reactions between strains that accumulate as people are exposed to multiple viruses over their lives. The breadth of such cross-reactions was determined by previously reported antigenic evolution . In simuWe found associations between the phase of antibody response cycles and the risk of seroconversion to circulating strains after accounting for the homologous preexisting HI titers. Due to the low influenza vaccine coverage in our participants and in China in general, the observed seroconversions likely reflected antibody responses after natural exposures during the study period. Previous studies have reported differential risk in individuals with the same homologous HI titer, proposing that unknown individual exposure histories and cohort effects are possible explanations . Our finWe demonstrate that resonance of cycles in individual-level antibody responses could form variations in phase distribution of antibody responses across birth cohorts, which is consistent with previous findings that the fraction of A(H3N2) associated cases across different birth cohorts was found to change year to year . Our resIn this study, we did not explore the interactions between individual-level antibody responses with population-level A(H3N2) activity . We minimized the impacts from population level by performing the Fourier analysis with individual departures from population average and validating the results with data from the Vietnam cohort. Simulation results further suggested that the population-level virus activity alone was not able to recover the observed periodicity, though epidemics with less regularity seemed to increase the variability in individual-level periodicity in the presence of broad cross-reactions .We recognize that HA-binding antibodies only mediate about half of the protection against influenza infections, while other forms of immunity, including neutralizing antibodies, non-HA head-specific inhibitory antibodies, and cellular immunity, would provide independent protections against infection and the severity of the diseases . The breOur work has several limitations. First, between-subtype interactions have not been incorporated into our framework. It is arguable that whether infections with A(H1N1) could confer years-long cross-protections against A(H3N2) in humans at individual level , while pThe following institutional review boards approved the study protocols: Johns Hopkins Bloomberg School of Public Health (IRB 1716), University of Florida (IRB201601953), University of Liverpool, University of Hong Kong (UW 09-020), and Guangzhou No. 12 Hospital (‘Research on human influenza virus immunity in Southern China’). Written informed consent was obtained from all participants over 12 years old; verbal assent was obtained from participants 12 years old or younger. Written permission from a legally authorized representative was obtained for all participants under 18 years old.We used serum collected from 777 participants who were recruited to an ongoing Fluscape cohort in Guangzhou, China, and provided blood samples for both a baseline visit (December 2009 to January 2011) and a follow-up visit (June 2014 to June 2015) . The cohWe measured antibody titers against 21 A(H3N2) strains using HI assays of paired serum collected from the two visits . StrainsTo extract population- from individual-level A(H3N2) activity, we fitted a GAM of log HI titers  on the swhere analysis .Residuals were calculated as the difference between observed and predicted log-titers from the fitted GAM to characterize individual-level A(H3N2) immune responses . A time and then chronologically ordered by the year of strain isolations. HI titers for baseline and follow-up visits were fitted separately, and only titers to strains that were isolated after the person was born were included in the model.Periodicity in individual antibody responses to influenza was examined using Fourier spectral analysis with linear detrending, from which variances explained by each frequency were extracted . As the For individual-level periodicity, we extracted the frequency that explained the most variance  for each individual  and plotThe peak frequency of the Fourier spectrum we extracted only represents the frequency that explained the most variance, but it cannot reflect the variance explained by the other frequencies, that is, whether the Fourier spectrum is skewed toward the peak frequency or is flatly distributed . Thus, wwhere utations .To examine the impact of using irregularly sampled intervals and interpolation on the results from the Fourier spectrum analysis, we performed a sensitivity analysis using the Lomb–Scargle periodogram , which iAlthough we removed the linear trend before applying Fourier analysis, several time series contained nonlinear trends that could potentially bias the estimate of the peak frequency to lower values , we dropped 1 out of the 21 strains and repeated the Fourier analysis to the time series of the remaining 20 strains. For the permutation test, we shuffled the time series of the remaining 20 strains and reinterpolated the shuffled time series for each individual. Results suggested that dropping out one strain did not affect our conclusions  and 3.We tested the robustness of our results from the Fourier analysis with a time series of 21 irregularly sampled data points with the same time resolution as our data. Time series consisted of random values generated from varying underlying distributions. Briefly, we drew a set of random values for each individual and the length of time series was based on the individual’s year of birth. We performed the interpolations, Fourier analysis, and extracted the peak frequency of the Fourier spectrum for each new time series. Finally, the distribution of peak frequencies for the simulated time series and their null distributions from permutations were compared.We performed this analysis using values drawn from normal and lognormal distributions without periodicity . In addiWe then applied the Fourier analysis on time series generated by sampling from periodic curves with white noise. To do this, we first simulated a time series from 1968 to 2014 on a yearly basis for each participant from a sinusoidal curve with a certain periodicity and white noise. We then subset the simulated time series to the years when our tested A(H3N2) strains were isolated relative to each participant’s year of birth. We applied the previously described interpolation and Fourier analysis to the subset of each time series. We repeated the above analysis for 777 participants and compared the distributions of peak frequencies from simulated time series and their 1000 permutations . Four scTo examine the effects of participants who had a relatively shorter exposure history of A(H3N2) on the reported cycles, we repeated the Fourier spectrum analysis with time series of residuals for a subset of participants (n = 487) who could have experienced all tested A(H3N2) strains,that is, born before 1968. The analysis follows the same steps as the main analysis except that the distributions of peak frequencies were plotted across 487 eligible participants. Cycles with low frequencies were found for the subset of senior participants as well, with an increasing proportion of participants having the lowest frequency .In order to test our results with a different population, we repeated the analysis with publicly available data reported in a previous Vietnam study . LongituIn the Vietnam study, multiple strains had been isolated in the same year, resulting in multiple titers being available for a given year for each individual. Therefore, we fitted a cubic spline in order to derive a time series that captured the geometric mean titers to strains isolated in the same year. We then applied Fourier analysis to each splined time series and extracted the peak frequency of each spectrum. The distribution of peak frequencies was characterized across 69 individuals by the year of serum collection . For theIn order to explore the mechanisms behind the reported dynamics of human immune responses to influenza, we applied a previously described mechanistic model  to generConstruct initial antibody profile. An initial antibody profile was generated for the sera collected in 1968 for participants who were born on or before 1968, or the year of birth for participants who were born after 1968. Titers to all 47 strains were assumed to be 0 for initial antibody profiles.Extract preexisting titers for each season. For an examined year  year y) .Determine the probability of infection of the circulating strain. The probability of an individual infected by the strain isolated in year otection . For theSimulate infection event. Infection outcome was randomly generated following a binomial distribution with the probability calculated in step 3. Infection outcomes were simulated for each individual every year.Update immune responses. Immune responses  to the whole panel of strains were updated based on the annual infection outcome  to 2014 and extracted the antibody profiles to all 47 strains in 2014 for further analyses.In order to explore the mechanisms that created the observed cycles, we performed simulations under different scenarios that considered several generally recognized components of immunity :Baseline scenario , which aPopulation activity-only scenario , which aNarrow cross-reaction scenario , which aBroad cross-reaction scenario , which aCross-reaction-only scenario , which aNo population activity-only scenario , which aRandom population activity scenario , which aPeriodic population activity scenario , which aWe adapted the previously described model to simulate immune responses after exposures ; the parwhere The antigenic seniority was scaled by a suppression parameter Prior study estimated Finally, the titer against strain where For the baseline scenario and cross-reaction-only scenario, the probability of infection was assumed to be a constant. For the cross-protection and antigenic seniority scenarios, a higher HI titer to a circulating A(H3N2) strain is assumed to be associated with lower risk of infection with that strain . We assudeled as (10)pI|μ where  studies . After aThe annual A(H3N2) activity, Uniform, where annual activity varies between 0 and 0.2 randomly.The main objective of this analysis was to demonstrate that population circulation alone was not able to recover the observed periodicity in individual antibody responses. Thus, although there remain debates about the interactions between influenza subtypes, we showed that it seemed not to be the main driver of the observed periodicity in individual antibody responses.We estimated the phase angle , II (91–180°), III (181–270°), and IV (271–360°) . Of noteWe predicted the phase angle (in degree) for the strain that circulated in 2012, which is the middle between our baseline (2010) and follow-up (2014) visit. We first fitted To assess the consistency between the prediction and observation of phases in 2012, we plotted the distribution of the observed phase in 2012 among people who were predicted to in each of the four phases in 2012 .We examined the association between the phase in individual antibody responses and antibody responses to circulating strains . We measTo examine the differences in phase distribution across different birth cohorts, we first estimated the observed individual phase in 2012 by fitting We estimated the phase distribution across birth cohorts using the predicted phase in 2012, which was derived by fitting We examined the association between the predicted cohort-specific proportion of phase IV and the observed proportion of seroconversion to either A/Texas/2012 or A/HongKong/2014 using Pearson correlation .https://github.com/UF-IDD/Fluscape_Periodicity, . We used the ‘mgcv’ package to fit GAMs . The Lom This article follows the still unanswered concept of ‘original antigenic sin’ and shows the existence of a 24-year periodicity of the immune response against influenza H3N2. The valuable work suggests a long-term periodicity of individual antibody response to influenza A (H3N2) within a city. public reviews designed to be posted alongside the preprint for the benefit of readers; (ii) feedback on the manuscript for the authors, including requests for revisions, shown below. We also include an acceptance summary that explains what the editors found interesting or important about the work.Our editorial process produces two outputs: (i) Decision letter after peer review:eLife. Your article has been reviewed by 2 peer reviewers, and the evaluation has been overseen by a Reviewing Editor and Betty Diamond as the Senior Editor. The following individuals involved in the review of your submission have agreed to reveal their identity: Koji Tokoyoda (Reviewer #1); Marcus Robinson (Reviewer #2).Thank you for submitting your article "Long term intrinsic cycling in human life course antibody responses to influenza A(H3N2)" for consideration by The reviewers have discussed their reviews with one another, and the Reviewing Editor has drafted this to help you prepare a revised submission.Essential revisions:1) The model needs to be tested with different parameters to show that a 24-year periodicity is best. In other words, what would happen to the predictions if the periodiciyty were 35-year or 6-year?2) The strains inducing high HI titers may have similar mutations and may be reactive to the same antibodies. What are the mutation frequencies among 21 A(H3N2) strains? So, generating a map of antigenic distance for the strain used is required.3) As seen in the 2) comment of reviewer #1, reinfection/vaccination at any time point confounds the overall titers and seroconversion. Hence, considering this point, authors need to make a more explicit discussion on how these are controlled.Reviewer #2 (Recommendations for the authors):This manuscript should be evaluated by a biomathematician to cover the appropriateness and form of the spline equations and the Fourier transform approach.The introduction should discuss original antigenic sin (OAS) and its foundations and would benefit from discussing anamnestic Ig responses against previously encountered strains a little more than it does currently.L95: (Please insert 'and' between '2021),' and 'antigenic'. Essential revisions:1) The model needs to be tested with different parameters to show that a 24-year periodicity is best. In other words, what would happen to the predictions if the periodiciyty were 35-year or 6-year?Thank you for your comments. As suggested we assumed a 35-year and 6-year periodicity and repeated analyses in the “Disentangling cohort effects using cycles in individual antibody responses” section . Results suggested that model predictions with either 35-year or 6-year periodicity failed to outcompete the model predictions assuming a 24 year periodicity . For example, the observed proportion of seroconversion to circulating strains in each cohort have correlation coefficients of 0.49 , 0.63  and -0.12  with the predicted proportion of phase IV when assuming a 35-, 24- and 6-year periodicity, respectively. The higher correlation and statistical significance suggests that the 24 year periodicity has greater association with the proportion who seroconvert.We also hope to clarify that the 24-40 year periodicity  was identified from the observed data using Fourier spectrum analyses, which is robust to a number of analytic and sampling variations. The 24-year periodicity was used as it was shared by most of the participants . While we acknowledge that the exact value for this long-term periodicity may depend on the number and span of the tested strains, we had strong support for this cyclic variability in our data. For analyses in Figure 4, we also used an individual periodicity that was determined by each individual’s peak frequency  and found consistent results, further supporting that the 24-year periodicity was shared by most of our participants. We included this discussion in the main text (lines 337-340).To address this comment, we also included the results of analyses using alternative 35- and 6-year periodicity  in Figure 4 —figure supplement 1, and reported these results in the main text (lines 262-264).2) The strains inducing high HI titers may have similar mutations and may be reactive to the same antibodies. What are the mutation frequencies among 21 A(H3N2) strains? So, generating a map of antigenic distance for the strain used is required.Thank you for your comments. Our selection of the 21 tested strains were selected to span the circulation duration of A(H3N2) strains since 1968, when this subtype emerged. We also prioritized strains that were included in vaccine formulations as well as those tested to create the antigenic map by Fonville et al. [1].As suggested by the editors and reviewers, we reproduced the antigenic map (up to strains isolated in 2010) using previously published data [1]. We compared this with our tested A(H3N2) strains in Figure 1—figure supplement 3. The figure shows that the 21 strains (or their belonging antigenic clusters if the strains were not used for the map) largely tracked the antigenic evolution of A(H3N2) since its emergence in 1968, with a reported mutation rate of 0.778-unit changes in antigenic space per year .We further calculated the paired antigenic distance of strains tested in the antigenic map, which was highly correlated with the time interval between the isolation of the two strains. The figure also suggested our tested strains cover the time spans and antigenic distances that were shown in the original antigenic map. In addition, our observed periodicity was identified in individual time series of residuals, which has removed the shared virus responses or assay measurement effects . Therefore, we believe that the impact of specific mutations may have been limited in our findings.To address this comment, we included the reproduced antigenic map showing the locations of the tested strains and their pair-wise antigenic distance in Figure 1—figure supplement 3 and referenced in the main text (lines 127).3) As seen in the 2) comment of reviewer #1, reinfection/vaccination at any time point confounds the overall titers and seroconversion. Hence, considering this point, authors need to make a more explicit discussion on how these are controlled.Thank you for your comments. We agree with the editor and reviewers that the observed seroconversion of the circulating strains may reflect responses after vaccination or recent infections. The overall influenza vaccination coverage in China has been <5% . Only 1.3% (10 out of 777) of our participants self-reported with any influenza vaccination history at baseline, and only 5 individuals reported vaccination at follow-up [5]. Therefore, we believe that the observed periodicity and seroconversion pattern in our study was unlikely to be attributed to recent influenza vaccinations.Instead, we believe that the observed seroconversions to circulating strains between our baseline and follow-up were likely due to pervasive exposure or infection with influenza ; 98% and 74% of participants experienced 2- and 4-fold rise to any of the 21 tested A(H3N2) strains [5]. However, recent re-exposures or re-infections would not substantially affect our observations on the long-term periodicity in individuals antibody responses. The strongest evidence for this is that we found similar periodic behaviour in the two time points of sample collection that we report in this manuscript. We repeated the Fourier analyses for serum collected at follow-up , and found that the long-term periodicity was still detectable .In fact, we believe that repeated exposure contributes to the periodic phenomenon that we report. Repeat exposures to A(H3N2) have been found to subsequently lead to antibody responses that were affected by pre-existing antibodies and cross-reaction . We incorporate re-infections in our simulations, with and without subsequent cross-reaction with previously exposed distant strains . Results indicate that reinfection alone cannot recover the observed long-term periodicity , while reinfection plus the resulting cross-reaction can recover such long-term periodicity .In summary, we believe that repeated exposures or re-infections are integral to the generation of our reported periodicity, hypothesizing that they may be drivers of life-course antibody profiles and the observed periodicity. However, exposures proximal to serum collection do not appear to substantially affect the observed periodicity, as we see consistent results from sera taken from individuals ~3-4 years apart.To address this comment, we reported the vaccination status of our participants when introducing the data (lines 127-129) and in the discussions (lines 280-282 and 313-315):“Only 0.6% (n = 5) of participants self-reported influenza vaccinations between the two visits, therefore, the observed changes in HI titers between the two visits were likely due to natural exposures.”“Due to the low influenza vaccine coverage in our participants and in China in general, the observed seroconversions likely reflected antibody responses after natural exposures during the study period.”“Particularly, our simulation results suggested that model included repeated exposures or population level A(H3N2) activity alone did not recover the long-term periodicity .”Reviewer #2 (Recommendations for the authors):This manuscript should be evaluated by a biomathematician to cover the appropriateness and form of the spline equations and the Fourier transform approach.Thank you for your comments. The core model and parameter values that we used in the study has been previously published in the peer-reviewed journals. The Fourier transform is a classic method with several widely acknowledged R packages available. To insure the appropriateness of the options of the Fourier transform approach, we conducted several sensitivity analysis to check the robustness of our results. Specifically, we obtained similar results when using different spline methods and a different transform approach . Simulation-and-recovery experiments suggested that power of our Fourier transform approach and suggesting that the observed periodicity was not able to be recovered in non-periodic time series (i.e. a permuted version of the data) .The introduction should discuss original antigenic sin (OAS) and its foundations and would benefit from discussing anamnestic Ig responses against previously encountered strains a little more than it does currently.Thank you for your comments. We expanded our introduction with more discussion about OAS in the second paragraph.“Original antigenic sin (OAS) is a widely accepted concept describing the hierarchical and persistent memory of antibodies from the primary exposure in childhood. Recent studies suggested that non-neutralizing antibodies acquired from previous exposures can be boosted and may blunt the immune responses to new infections.”L95: (Please insert 'and' between '2021),' and 'antigenic'.Revised, thank you.


Higher acuity levels in COVID-19 patients and increased infection prevention and control routines have increased the work demands on nurses. To understand and quantify these changes, discrete event simulation (DES) was used to quantify the effects of varying the number of COVID-19 patient assignments on nurse workload and quality of care. Model testing was based on the usual nurse-patient ratio of 1:5 while varying the number of COVID-19 positive patients from 0 to 5. The model was validated by comparing outcomes to a step counter field study test with eight nurses. The DES model showed that nurse workload increased, and the quality of care deteriorated as nurses were assigned more COVID-19 positive patients. With five COVID-19 positive patients, the most demanding condition, the simulant-nurse donned and doffed personal protective equipment (PPE) 106 times a shift, totaling 6.1 hours. Direct care time was reduced to 3.4 hours (-64% change from baseline pre-pandemic case). In addition, nurses walked 10.5km (+46% increase from base pre-pandemic conditions) per shift while 75 care tasks (+242%), on average, were in the task queue. This contributed to 143 missed care tasks (+353% increase from base pre-pandemic conditions), equivalent to 9.6 hours (+311%) of missed care time and care task waiting time increased to 1.2 hours (+70%), in comparison to baseline (pre-pandemic) conditions. This process simulation approach may be used as potential decision support tools in the design and management of hospitals in-patient care settings, including pandemic planning scenarios. Excessive nurse workload leads to overtime, increased error rates, presenteeism, absenteeism, burnout, decaying worker morale, decreased performance and injuries including work-related musculoskeletal disorders (WMSD) –7. Pre-pA further concern in the pandemic lies in the extra workload for HCPs associated with IPAC protocols. Pre-pandemic research suggests that HCP workload was already near a maximum . The addDiscrete Event Simulation (DES) is an operations research approach , that caThe aim of this research, therefore, was to develop, validate and test a DES modelling approach that can quantify nurse workload and care quality parameters as the number of COVID-19 positive (C+) patients assigned to a nurse increases from 0 to 5 in a 5-patient assignment scenario for a specific hospital care unit. In doing this, the model will isolate the impacts of both the extra IPAC routines, and the increased patient severity associated with C+ patients on nurse workload and care quality outcomes.The DES model simulates the process of care delivery on a task-by-task basis. The model was created using the commercial version of Arena (Rockwell Automation). Modelling was conducted on a medical-surgical unit in a large urban teaching hospital that was upgraded to C+ status to accommodate the rapid influx of C+ patients. The unit was tasked with providing care to C+ patients during the second wave of the pandemic (October—December 2020). Research ethics approval was received from the healthcare institution and university research ethics boards. All participants were recruited using informed consent procedures. Model inputs  in Canada must complete a four-year baccalaureate degree from a post-secondary university nursing program or a collaborative college-university nursing program . RN educTo understand how C+ patients have impacted the nurse’s ability to provide care, multiple focus groups were conducted (n = 5) with 12 experienced RNs with 2 to 27 years of experience. To ensure consistency, participants were selected from the same unit from where the patient care data and unit layout was collected. Nurses reported that some care tasks for C+ patients have increased frequency (up to 50%) while other tasks remain unchanged. Similarly, some task durations were noted to be longer for C+ patients. As an attempt to represent the most nurses in the modelling algorithms, the changes in acuity levels for C+ patients were determined by using mode value . ParticiHospital IPAC policy to protect against COVID-19 transmission required nurses to don and doff personal protective equipment (PPE) each time they entered or exited a C+ patient’s room. These posed additional tasks for nurses caring for C+ patients, compared to pre-pandemic work procedures and C- patients. The above-mentioned focus groups also revealed that RNs must wear a medical-grade safety mask throughout the shift. In addition, hospital policy dictates nurses must also wear safety glasses/safety shields, gown and gloves when attending to COVID-19 positive patients. The time duration required to don and doff the additional PPE was determined with a time and motion study observing 20 repetitions of donning and doffing with eight different nurse participants. These specific IPAC routines were necessitated by the pandemic and are specific for C+ patients only.Unit layout, the physical architecture of the system, defines walking and transport distances in the unit. The unit’s physical dimensions were measured using a laser measuring instrument (Bosch GLM30 100 ft.). These dimensions were used to create a scaled layout drawing using Visio (Microsoft) and were then programmed into the DES model.care delivery logic determines the sequence of tasks and associated movements of the simulant nurse to deliver the required care. The critical elements of this logic include a) care task walking patterns, b) care task priorities, and c) care task sequence rules.The Care Task walking patterns–These entail the walking route of a simulant nurse while delivering a given care task. These require walking to the appropriate storage location if materials or medications are needed. Similarly, if spent or dirty materials must be disposed of after a care task, then this also requires walking to the disposal location on the unit. These required walking patterns were established in interviews with two subject matter experts (SMEs), RNs with 8 and 12 years of experience working in the study unit.Care Task priorities–These denote the relative importance or urgency of each care task, which was determined through a series of individual interviews and focus group discussions with 36 experienced RNs with 2 to 27 years of experience from the study unit. Nurses in each data-gathering session worked together to generate a priority ranking. A mode [. A mode  priorityCare Task sequence rules–This determines how the simulant nurse prioritizes and executes tasks in the DES model These were determined with the same group of nurses as for task priority. Nurses in all sessions unanimously agreed they deliver the highest priority task to the patient that is closest to their current location. However, when attending to C+ patients, nurses reported “bundling” as many care tasks as they can, so they would not have to don and doff PPE multiple times. These bundles of tasks were determined in the same data collection events with unit nurses, and the most common bundling strategy was used for the experimental analysis. It is noteworthy that, while it is possible to run the model to explore the impacts of different logics, priorities, or bundling strategies, the intent of this current study was to establish a consistent and reasonable baseline scenario that could be used for the comparative experiment.These key inputs described above  model verification checks, b) model validation, and c) experimental tests of the impacts of the number of C+ patients in a standard five patient assignment.Model verification consists of a series of checks aimed at confirming that the model operates as intended . These wFace validity–The modelling results were shown to the subject matter experts  to determine if the DES model was producing realistic results [ results . They reExtreme condition test–The DES model was run on extreme conditions to check if the model would provide expected results. In this study, we tested two conditions: a simulant-nurse was assigned to 2 and 12 patients. As anticipated, the mental workload and care task waiting time increased extensively for 1:12 and decreased for 1:2 conditions compared to the 1:5 condition.Output relationship correctness test–This technique explores the relationship of two dependent outputs. The DES model was run on the conditions 1:3, 1:4, 1:5 patients, all being C+. The number of times PPE is donned and doffed, and the cumulative donning and doffing time both saw equidistant increases.TM Alta Tracker with a step counter  worn one walked . FitbitTre steps , 47. There steps . Bartko’re steps  intraclaDetailed information on the development of such modelling approaches. A detailed explanation of the development of nurse-focused simulation models can be found at Qureshi [ Qureshi  and Qure Qureshi .The nurse-patient ratio of 1:5 was kept in all conditions, a standard in most in-patient units . In thisGeographically, the simulant nurse was assigned to the typical bed assignment. In an interview with 19 RNs with 2 to 27 years of experience in the selected unit, they were shown a simplified blueprint drawing of the unit and were asked to draw the bed assignments for the past 10 shifts. Out of the 171 geographical bed assignments, the most common geographical shift assignment identified was selected. This assignment consisted of two beds near the nurse station, two beds far away from the nurse-station and one bed at an intermediate distance (between the far end and center of the unit). This represented a typical assignment on the unit and was kept constant for all tests.The DES model represents “day” shifts, each consisting of 12-hours, with no breaks. Each condition was run for 365 shifts, calculated using the method of Banks and colleagues , to captThis study has been approved by the Research Ethics Board (REB) of Toronto Metropolitan University  , and field study site (reference # 20–5409). All participants provided written consent to participate in this study.An overall ICC of 0.91 was observed for the comparison of actual nurse walking distances to those of the matched simulant-nurse. The simulant nurse walked an average of 7.7 km in a 12-hour shift , based on an average of 10041 steps . In comparison, the actual nurses walked an average of 8.1km , based on 10567 steps . Individual comparisons are presented in The DES successfully quantified the impact of COVID-19 patient assignments. The results are presented below.Direct care time. As illustrated in PPE donning and doffing. Donning and doffing increased as C+ patients increased, ranging from 31 to 106 times in a 12-hour shift (donning and doffing = 1 count). Nurses spent a cumulative time of 2 hours (at 1C+)to 6.1 hours (at 5C+)donning and doffing. Mental workload. The average across shift number of care tasks “in queue”, a mental workload indicator, ranged from 22 at baseline, rising steadily to a shift average of up to 75 tasks waiting when assigned to 5 C+ patients to 10.55 km (5C+). A plateau effect was observed for conditions attending to three, four and five COVID-19 patients  patients assigned. We do this by adapting the DES model of an acute care medical-surgical unit , to reflAs the results demonstrated in the pre-pandemic condition, a nurse’s workday was already loaded with more tasks that could be completed in a 12-hour shift—a finding consistent with earlier modelling efforts . As nursrushing care tasks” and rushing how they wear PPE in order to deliver the same level of care during the pandemic. This rushing increases the risk of infection transmission [During the focus groups to discuss how COVID-19 has modified work demands, participants revealed they are now “smission . This fusmission . Rushingdistance walked by nurse showed a plateau effect in which the distance walked had little increase beyond 3 COVID+ patients; less than 0.2 km increase over the 3 conditions This can be interpreted as a plateau effect due to the inability to complete additional care tasks; meaning there was no more time available to attend to additional care tasks and therefore walking for care delivery, and any additional work will simply increase the number of undelivered tasks. This places nurses in the difficult position of having to either rush to complete tasks, with consequent negative implications [Model outputs of ications , stay onications . Nurses ications . This antasks in queue. The average number of tasks waiting to be performed increased by up to 242%, from 22 tasks in pre-pandemic condition to up to 75 care tasks when caring for 5 C+ patients. Given these increased demands, care task waiting time increased up to 1.2 hours, a 70% increase from the baseline condition. The modelling capability developed in this study can be used to examine the mental burden and test strategies to better manage the mental and physical burden.The DES model reports over 140 tasks being missed, or left undone, at the end of a shift when caring for 5 C+ patients. This is consistent with previously published research where missed care was increased when attending to more acute patients . Of the In simulation model terms, the priority and movement logics for given tasks, and critical inputs to the model, will determine the sequence of execution and, therefore, the nature of tasks that remain incomplete at the end of the simulated shift. By making these priorities and their impacts explicit, this modelling opens the door to revisit and adapt task assignment and priority policies in ways that might minimize the negative effects of the extra workload associated with IPAC routines. Pre-pandemic, it has been reported that some nurses had started to “bundle” care tasks to reduce walking and to streamline care delivery . During hospital managers understand the implications of different scenarios on their staff–and hence on the system as a whole–in a range of possible scenarios. This research has implications for the design of the healthcare system as a whole, including pandemic planning scenarios. Furthermore, this research illuminates and quantifies the workload issues experienced by nurses on the COVID-19 front lines.The modelling approach demonstrated here integrates available evidence and data to help understand the complex system dynamics including, nurse outcomes (workload) and patient outcomes . In this view we model healthcare units as sociotechnical systems , 74 in wadministrators understand and predict the impact of work design decisions (e.g. staffing ratios and skill mix) on nurse workload and thereby the quality of care, while policymakers can test the consequences of policy tradeoffs and technical design. Architects and technology developers can examine the workload and care quality implications of different design choices they are considering. Meeting the needs of these stakeholders will require a more routinized approach to model creation which currently requires resource-intensive and extensive in-data collection in order to program.Healthcare poses a complex system  with manThe modelling approach demonstrated here is, by necessity, incomplete. Future work includes adding new model capability to quantify the biomechanical workload and associated musculoskeletal disorder risks faced by staff due to the physical workloads . This caBeyond model capability, there is a need to extend research on the application and use of such tools by key decision-makers in practice. How can such models be deployed in ways that minimize model development time to create useful decision support tools? Further field research is needed to examine the application domain of these models. There is also a need to extend these models into healthcare systems beyond the medical-surgical unit examined here. In this study a medical-surgical unit was selected because the largest proportion of acute care nurses across Canada (24%) work in this area . Long Teth birth anniversary of the founder of modern nursing–Florence Nightingale [This study presents an approach to developing and applying DES to quantify nurse workload and care quality outcomes during COVID-19 pandemic conditions. The model was found to be valid in comparison to actual nurse walking distances with an ICC of 0.91. This study provides quantifiable evidence of the work overload nurses are experiencing during this pandemic and its implications on the quality of care. Increasing the number of C+ patients in a nurse’s assignment, substantially increased nurse workload and decreased the quality of care. Nurses were found donning and doffing PPE up to 106 times a shift, translating into 6.1 hours donning and doffing PPE when caring for 5 C+ patients. This contributed to reducing direct care time down to 3.4 hours. In addition, nurses in this 5 C+ scenario walked up to 10.55km per shift while managing an average of up to 75 care tasks in the task queue. While caring for 5 C+ patients, missed care increased up to 142 tasks, and missed care working time increased up to 9.6 hours, and. care task waiting time increased up to 1.2 hours. This pandemic has pushed an already overworked population of nurses, beyond their limits. 2020 was the 200htingale –is this The modelling approach used here has the potential for use in a wide range of scenario testing. It is sensitive to architecture, care delivery routines and policies, patient characteristics, and even nurse preferences in care delivery approaches. While the model shows promise for decision support, as it integrates the effects of a wide range of decisions normally made by diverse stakeholders, further study is needed to see how this information can be made accessible and useful to these stakeholders to support meaningful improvements in the design and management of healthcare systems under both normal and unexpected operating scenarios such as future pandemics.S1 Data(XLSX)Click here for additional data file.


Amdam et al., reported on the effect of vaccination history on the immune response to H1N1pdm09 vaccines in health care workers during the period 2009-2014, and Juvet et al., summarized data obtained in the years following the 2009 H1N1 pandemic on vaccine safety for the rolled-out pandemic influenza vaccines.For over a hundred years, humanity was confronted with recurring pandemics caused by influenza viruses, over-burdening health care systems and disrupting societies worldwide. Since the beginning of the previous century, we have encountered four pandemics caused by H1N1, H2N2, and H3N2 viruses, with the last one in 2009 caused by an H1N1 swine-origin virus. With every pandemic a new influenza virus emerged that started circulating in a human population that was immunologically naive against the emerging virus. This condition allows us to study the response to a new vaccine that targets the emerging virus in the absence of pre-existing neutralizing antibodies that may affect vaccine effectiveness. In this Research Topic, Liu et. al., discussed a strategy based on targeting conserved epitopes in the influenza B HA using an engineered inactivated influenza B virus vaccine to induce antibody-mediated protection that correlates with ADCC. A strategy is also being tested in the clinic for an Influenza A universal vaccine that uses chimeric HA influenza vaccines -specific CD8+ T cells. The nucleoprotein is a conserved influenza T cell antigen also in humans, and therefore targeting the NP may result in protection against influenza viruses from different subtypes. It was already suggested that infection induced NP-specific CD8+ T cells correlated with protection from influenza re-infection for several decades  has been suggested as a good universal vaccine epitope, and antibodies targeting M2e correlated with protection in preclinical and clinical settings  influenza viruses from the animal reservoir. Therefore, H5N1 and H7N9-based pandemic vaccines have been tested in humans. Zhou et al., reported that an adjuvanted H5N1 vaccine can efficiently induce HA- and NA-specific antibodies in healthy volunteers that are protective in a preclinical animal model. Finally, preventing spillover of influenza viruses from the animal reservoir may reduce the risk of future pandemics. The one health approach is based on the concept of controlling viruses with zoonotic potential in animal reservoirs, for example poultry, not only to limit circulation of viruses with pandemic potential in the reservoir but also to prevent spread to humans. Kong et al., described in this Research Topic a virus-like particle supplemented with epitope antigens that can be effectively used to prevent H7N9 avian influenza virus from being shed by experimentally infected chickens.There is a constant fear that a new and potentially highly pathogenic influenza virus will make the cross-species jump from the animal reservoir to humans and start a new pandemic. Therefore, it came as a surprise for many of us when the second pandemic of this century was caused by SARS-CoV-2, a coronavirus. We are now over two years into the COVID-19 pandemic, and effective vaccines have saved many lives and allowed us to go from worldwide lockdowns towards less restricted travel. The fast response to this newly emerging coronavirus, also from an animal reservoir, was at least partly possible due to pandemic preparedness guided by our knowledge from previous preclinical and clinical research in the context of influenza virus-host interactions, influenza epidemiology, and influenza vaccine development. Interestingly, due to measures in place like social distancing, masking and creating awareness for respiratory virus transmission during the COVID-19 pandemic, hospitalized cases due to influenza virus dropped drastically , 4. Thisvaccines , 7. Del settings , 11. Howsettings . Li et aPandemic Influenza Vaccine Approaches: Current Status and Future Directions provided an interesting and timely update on the current research activities progressing in a highly relevant area of vaccine development that is part of an overall pandemic preparedness strategy, and the editors appreciated the many excellent contributions by several scientific teams.In summary, All authors contributed to the preparation of the manuscript and reviewed the final content prior to journal submission.CM is an employee of the GSK group of companies and reports owning shares in GSK.The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Adulterations of olive oil are performed by adding seed oils to this high-quality product, which are cheaper than olive oils. Food safety controls have been established by the European Union to avoid these episodes. Most of these methodologies require expensive equipment, time-consuming procedures, and expert personnel to execute. Near-infrared spectroscopy (NIRS) technology has many applications in the food processing industry. It analyzes food safety and quality parameters along the food chain. Using principal component analysis (PCA), the differences and similarities between olive oil and seed oils  have been evaluated. To quantify the percentage of adulterated seed oil in olive oils, partial least squares (PLS) have been employed. A total of 96 samples of olive oil adulterated with seed oils were prepared. These samples were used to build a spectra library covering various mixtures containing seed oils and olive oil contents. Eighteen chemometric models were developed by combining the first and second derivatives with Standard Normal Variable (SNV) for scatter correction to classify and quantify seed oil adulteration and percentage. The results obtained for all seed oils show excellent coefficients of determination for calibration higher than 0.80. Because the instrumental aspects are not generally sufficiently addressed in the articles, we include a specific section on some key aspects of developing a high-performance and cost-effective NIR spectroscopy solution for fraud detection in olive oil. First, spectroscopy architectures are introduced, especially the Texas Instruments Digital Light Processing (DLP) technology for spectroscopy that has been used in this work. These results demonstrate that the portable prototype can be used as an effective tool to detect food fraud in liquid samples. Economically motivated food adulteration, also known as food fraud, is carried out to minimize production prices and increase benefits. The adulterated product loses quality or may even become harmful. Adulteration with lower costs alternatives is common in high-demand products such as milk and in products of high economic value whose price has been increasing in recent years, such as olive oil.The limited production and high cost of the highly desirable olive oil make the olive oil susceptible to deliberated adulteration with low-cost and low-quality vegetable oils. The fat contained in such low-cost vegetable oils allows for conventional results to be performed to obtain satisfactory saponification values or refractive indexes, among others. This adulteration of olive oils with low-quality vegetable oils is a commercial fraud that can be focused on from two different points of view due to the quality of the product and a health and safety problem that is increased when the olive oil is consumed because of its nutritional benefits. Moreover, there are registered food safety occurrences in which oil adulteration was a major public health problem, such as in Spain in 1981. That year in Spain, several people died, and others suffered illnesses due to poisoning with adulterated oil . The devThe European Union has established food safety controls to avoid these episodes. Mostly, they are laboratory methodologies requiring expensive instruments, time-consuming and expert personnel to be carried out. Moreover, conventional methods cannot measure oil quality and safety analyses during certain processes, such as extraction or pressing. Some examples are high-performance liquid chromatography (HPLC) or gas chromatography (GC) with different detectors depending on the target analyte ,4. ThereNevertheless, controlling impurity is a challenge involving authorities and the olive oil industry, requiring analytical alternatives based on real-time analysis that can be carried out onsite by non-expert personnel. An analytical alternative is those strategies based on portable instrumentation, such as NIR procedures. However, this standard instrumentation sometimes does not have the required instrumental characteristics to successfully solve this trouble, which is necessary for developing new portable instrumentation adapted to olive oil analysis. In this research, we focused our work on the development of a NIR prototype, a portable sensor, cheap, easy to use and adapted to olive oils analysis. To test our instrumental proposal, we have developed qualitative and quantitative chemometric models to detect adulteration of olive oils with low-quality vegetable oils.The development of portable, small NIR spectrometers has advanced significantly over the past few years. These units operate using different optical diffraction grating properties, such as Fourier transform (Si-Ware NeoSpectra), linear variable filter (Viavi microNIR), and diffraction (Texas Instruments NIRscan Nano). Pasquini reviewed such instruments in a comprehensive review . An exteThis study used three different types of olive oil: extra virgin olive oil, virgin olive oil, and olive oil. Three varieties of seed oil adulterants, sesame oil, sunflower oil, and flax oil, were used for the mixture preparation. As can be seen in The oil mixtures were analyzed without pretreatment at room temperature (19 °C) in a standard quartz cuvette, 10 × 10 mm optical path, and a volume of 3500 mL . The measurements were carried out in transmittance mode, as depicted in Before proceeding with the spectra collection, the instrumental conditions were optimized. The scanning mode Column or Hadamard and the number of scans to be averaged were evaluated and compared to select NIR experimental conditions. Each sample was divided into five portions, scanning each one separately. After optimizing experimental NIRS collection conditions, the final spectrum was the average of all the spectra of the same sample. In order to optimize the number of spectra to the average for each sub-sample, an olive oil sample was collected by averaging 1, 5, 10, 15, 20, 30, and 50 scans in both scanning modes. Once the data were collected, the values of these five different spectra’ root mean square error (RMS) statistic were calculated ,15. Thisb number of scans.The NIRS spectra collected with the prototype were transformed into a data matrix with X and Y variables defined as wavelength (X) and absorbance data (Y). Chemometric strategies were developed with Unscrambler X software . Different strategies were developed to identify fraud in olive oil. One of them used categorical variables to identify the presence of seed oils in the olive oil samples, and the other one with the percentage of seed oil in the mixture to quantify the adulteration.Principal component analysis (PCA) and partial least squares (PLS) were the chemometric approaches tested to attempt these qualitative and quantitative strategies. PCA was employed to detect potential spectral outliers and classify the adulteration type. PLS was the regression procedure employed to build the calibration models using the global spectrum  . All theBefore PCA and PLS analysis, to minimize the light scattering phenomenon, the standard normal variation (SNV) mathematical treatment was applied to the spectra data matrix, on the raw data, and the data after applying different Savitzky–Golay (SG) derivative pre-treatments. To establish the successful model combination of pre-treatments  were tested in this study. The pre-treatment code used in this study can be summarized using a four-digit notation (a b c d), where the first digit (a) refers to the order of magnitude of the Savitzky–Golay derivative (SG) ; the second digit (b) indicates the polynomial order of the derivate; and the third (c and d) digits indicate the size of the left and right intervals respectively, used for the derivative smoothing calculation. A total of 18 different calibration models (3 parameters × 6 chemometric strategies) were developed using different pre-treatments of the olive oils samples and PLS as the regression approach. The pre-treatments employed were as follows: SG 1 2 4 4, SG 2 2 4 4, SG 1 2 4 4 + SNV, SG 2 2 4 4 + SNV, SNV + 1 2 4 4, and SNV + 2 2 4 4. And the effect of applying scattering correction, SNV before and after derivation was also tested.2), cross-validation determination coefficient (r2) values, and Range error Ratio (SECV/Range). For the choice of the best-fitting equation, it was also considered that the values of the calibration determination coefficient and the cross-validation coefficient did not have values particularly different from each other [The best mathematical pre-treatments were selected based on the statistical criteria for each adulterant oil. This selection was based on the lowest calibration standard error (SEC) and cross-validation standard error (SECV), as well as the highest calibration determination coefficient  is equipped in origin with a diffuse reflectance illumination module as shown in Light source: It consists of two lens-end Tungsten filaments. They are designated as lens-end lamps because the front end of the glass bulb is formed into a lens to direct more light from the filament to the sample test region. Tungsten halogen lamps are well suited as spectrometer light sources due to their broadband infrared radiation. The transmission module was equipped with two ILT 1088-1 lens-end lamps from International Light Technologies [nologies . Figure Slit: The input slit specifications affect the ability to couple light into the spectrometer and its spectral resolution. The slit width should be chosen to create an image width at the DMD corresponding to a desired spectral resolution. In addition, the slit length should be large enough to illuminate the full extent of the DMD panel, maximizing the system’s light throughput. Narrowing the slit width increases the spectral resolution of the system but requires a higher-performance optical system to sharply image the smaller slit onto the DMD [ the DMD .DMD array: The size of this component determines the maximum light collection area and the resolution of the system. The resolution of the system depends on the wavelength spectrum that is spread across one dimension of the DMD , the slit width, and the DMD pattern width. The other dimensions of the DMD  and the optical transfer function of the system determine the amount of light collected.Photodetector: For NIRS between 900 and 2500 nm, an InGaAs photodiode is the preferred photodetector due to its high quantum efficiency and responsivity. Typically, the photodiode produces a very small current signal proportional to its photosensitive area. A large photosensitive area produces more current in response to light at the expense of higher terminal capacitance. The higher capacitance will result in a slower response to light or lower bandwidth. This capacitance will also affect the capacitive feedback compensation : The ADC converts the voltage into a digital signal representation. For best results, an extremely low-noise, high linearity, high-resolution analog-to-digital converter minimizes the noise added by the conversion. To optimize the system’s digital resolution, the ADC’s dynamic range and reference voltages must be matched with the maximum photodetector signal. DLP NIRscan Nano uses the Texas Instruments 24-bit ADS1255.A key characteristic of this micro-spectrophotometer is that the relevant software can be downloaded from the manufacturer’s website, along with extensive hardware documentation and free access to routines for programming and communicating with it. It is essential for those who want to develop new applications for the instrument.Method: This controls the scanning process. The DLP NIRscan Nano comes preloaded with two scan configurations from the factory: Column or Hadamard. The Column scan selects one wavelength at a time. The Hadamard scan creates a set with several wavelengths multiplexed at a time and then decodes the individual wavelengths. The Hadamard scan collects much lighter and offers a higher SNR than a column scan [umn scan . HoweverWavelength range: Start and End wavelengths (in nm) or spectral range of interest for the scan. The minimum wavelength is 901 nm, and the maximum wavelength is 1700 nm.Width in nm: This number selects the width of the groups of pixels in the generated Column or Hadamard patterns. The options displayed correspond to the width of the dispersed spectrum in nm across the quantized pixel width.Digital Resolution: This number defines how many wavelength points are captured across the defined spectral range. This corresponds to the number of patterns displayed on the DMD during the scan. By increasing the digital resolution, the spectrum is oversampled. In general, set this resolution to oversample at least twice the desired full-width half maximum desired.Exposure Time: For scan configurations with one section, the exposure time is set to 0.635 ms. For scan configurations with more than one section, the exposure time can be individually set for each section in the range of 0.635 to 60.960 ms.Number of scans to average: This determines how many times the pattern set for a scan will be cycled through on the DMD. The microcontroller collects and averages the data, which lowers noise as additional scans are averaged. For scans where noise is a problem, this can be increased.Scan Reference Select: This button allows the user to choose the reference for the absorbance or reflectance graph. For transmittance sampling, data are taken with no sample in the cuvette.The second step is to enter the number of sections of a scan. A scan can be broken up into 1–5 sections. Scans with the same width and resolution should be done in only one section. More than one section must be created to create a fast scan with less resolution on wavelengths with less information and a higher resolution on wavelengths with areas of interest. Each section can have an individual set of the following parameters:Execute scan k times (we used k = 4) with scan time t and no time between scans.Compute the difference vector of the intensities at wavelength n.Compute the average of the intensity measurements at wavelength n.Compute the standard deviation of point 2.Compute the SNR as the ratio between points 3 and 4.Repeat points 1 to 5 at other wavelengths between 900 to 1700 nm.The distribution of SNR across the spectrum is critical , especially for a DMD-based spectrometer where uniform illumination across the mirror array is important. To test SNR at a particular scan setting, the following procedure was used :Execute Following the above procedure, The success of a NIRS methodology depends on the quality of the collected spectral information. Taking into account this consideration, the first step carried out in this study was the selection of the instrumental conditions. The developed prototype allows collecting spectra using two different scan modes, Column and Hadamard. In both modes, the final spectrum can be the average of a limited number of individual spectra. As detailed in As can be seen in All samples were scanned with the proposed prototype using previously detailed instrumental conditions. 2) second overtone vibration and broadband with a double peak between 1400–1500 nm due to O-H first overtone and C-H (CH2) combination [In the NIR range of the prototype, there are the following characteristic bands of the oil’s spectra. The bands observed around 1200 related with C-H  was carried out for each type of olive oil with its corresponding mixtures and all sample sets (N = 96) involved in this study. Different approaches were evaluated for PCA analysis using SG first and second derivatives. As can be seen in After evaluating the classification procedure, the next step was to perform the calibration models with all the spectra, using the PLS regression model and cross-validation with random groups. As indicated in 2) are higher than 0.74 except for sunflower oil applying SG 2 2 4 4 plus SNV pre-treatment (0.313). The coefficient of determination for cross-validation values is similar; all values are greater than 0.6 except for sunflower oil with the SG 2 2 4 4 plus SNV pre-treatment, whose value is 0.276. The highest R2 value is obtained with the SG 1 2 4 4 plus SNV pre-treatment for sunflower oil, whose calibration error (SEC) is 2.256.As can be seen in 2 values for each oil are quite similar for sesame oil and flax oil  by applying the different pretreatments. Still, the values of the coefficient of determination for the calibration of sunflower oil vary greatly depending on the pretreatment selected, ranging from the minimum value obtained 0.313 for SG 2 2 4 4 plus SNV to the maximum value 0.921 for the pretreatment SG 1 2 4 4 plus SNV. However, these marked differences, observed for R2, are not so evident for r2.An overall view of the results shows that the R2 ranged between 0.921 and 0.747, and the 1st derivative plus SNV as mathematical pre-treatment for adulteration with sunflower oil or with sesame oil, respectively. Values of coefficients of determination for cross-validation regression models , as shown in Regarding standard error (SEC and SECV), it should be noted that the low range of the adulteration assayed (2%) is within the detection limit of the proposed method, being necessary to increase variability in the low range and perhaps to develop a specific model depending on the adulteration.After obtaining and processing the spectra, the results shown in In this work, we presented and tested a portable and cost-efficient Near Infrared Spectroscopic prototype for fraud detection in olive oil. For this purpose, a transmittance module was developed for the Digital Light Processing NIRscan Nano EVM. Detection of fraud in liquid samples such as olive oils requires the collection of robust spectra with relevant information. All classification and calibration models showed satisfactory results. For all seed oils, the coefficient of calibration determination is greater than 0.80 and for cross-validation around or higher than 0.7. These results confirm the excellent characteristics of this portable prototype. It can be used as a tool to detect food fraud in liquid samples. We also included key instrumental considerations that should be considered in designing a hand-held Near Infrared Spectroscopic system.


Owing to their detachment from urban areas, people living in rural areas of Malawi are on the receiving end of health services and socio-economic benefits. The study therefore explored how Malawians living in these areas are adhering to coronavirus disease 2019 (COVID-19) containment measures and the factors that affect the COVID-19 fight amongst this population.The study investigated how the rural population in Malawi adheres to COVID-19 containment measures.The study was conducted in two districts in Northern Malawi.χ2) test was performed to determine the association between demographic variables and COVID-19 prevention practices and factors affecting the COVID-19 fight.Data were collected from 263 participants. The chi-square (p = 0.01), use of sanitisers (p < 0.01) and practising social distancing (p = 0.07). Monthly income was associated with the use of sanitisers (p < 0.01). Women were more exposed to fake news about COVID-19 (p = 0.09); older people were more likely to disregard COVID-19 containment measures for cultural reasons (p = 0.07); and monthly income was associated with a lack of resources for following COVID-19 containment measures (p < 0.01).Education was significantly associated with wearing of masks (Findings show that factors affecting the COVID-19 fight are influenced by various socio-economic factors which should therefore be taken into account in policy planning aiming at controlling the pandemic.The study provides health stakeholders with a policy direction that enhances better coordination, implementation and monitoring of COVID-19 response and recovery activities in rural areas in Malawi. The findings have implications on controlling current and future communicable diseases; the proposed strategies might be employed in fighting similar current and future pandemics. Some research that compared the coronavirus to the seasonal flu established that it is more contagious and deadly, with the median reproductive index  include avoidance of public gatherings, practising social distancing, washing hands frequently and wearing masks.9 Other scholars have made it clear that while biomedical practice is key in disease diagnosis and treatment, infection prevention and control (IPC) is fundamentally a behavioural science.10 However, research has shown that the implementation of these behavioural changes has been met with resistance in most countries. In the United States of America, for example, Americans perceive social distancing, quarantining and even mask-wearing as a violation of their freedom and personal liberty.11 Malawi may not be immune to such challenges; hence, it becomes necessary to assess the challenges that confront the fight against COVID-19, especially considering the prevailing complex cultural diversity and economic disparities in the country.A recent study has reiterated that change in human behaviour is the most effective strategy to mitigate the spread of the pandemic.12 The first case was confirmed on 02 April 2020, and new cases of infection and death have been recorded almost daily. Sensing the devastating threat that came along with the pandemic, the Malawi government enforced strict containment measures, including a 21-day national lockdown, which was, however, immediately set aside by the courts on the basis that it was enforced without consideration of people’s economic hardships.13 In light of this, the government relied on less strict behavioural change containment measures, namely wearing face masks, practising social distancing, avoiding crowded places, washing hands frequently, disinfecting rooms and surfaces and limiting times for opening shops and other business places such as markets and taverns. These containment measures have the potential to limit social and economic activities.12 The implication is that the longer it takes to control the pandemic, the longer the country’s economy and the health system will remain dysfunctional, leading to worsening poverty and possibly loss of lives of the greater population. Furthermore, despite repeated calls to adhere to these COVID-19 containment measures, very few people are practising the COVID-19 control measures12; for example, they continue to gather in large numbers, hence a high risk of infection and being infected. Lack of knowledge on the reasons why Malawians disregard some containment measures makes it difficult for the Malawi government and health stakeholders to effectively deploy containment measures that are more appropriate and appealing to the people.Being part of the larger global world, Malawi has not been spared from the COVID-19 pandemic.This cross-sectional study was conducted in some districts in the Northern Region of Malawi. However, this paper focuses only on two rural areas, namely Nkhata Bay and Rumphi, targeting rural communities. Rumphi district has the majority of its population living in remote areas largely deprived of access to health services, TV and mobile phones. Rumphi district also borders Zambia to the North West, where unchartered routes between the district and Zambia are numerous and institutionalised, leaving the district highly exposed to foreign COVID-19 transmissions. Relatedly, the majority of the population in Nkhata Bay lives in rural areas where access to health services, TV and radio remains problematic.The sample size was calculated with the Yamane formula as 263. A questionnaire was used to collect data from participants comprising mostly closed-ended questions. The questionnaire was translated into various local languages. The questionnaire addressed eight COVID-19 containment measures, namely wearing masks, using sanitisers, washing hands with soap, avoiding crowded places, practising social distancing and no longer shaking hands, going to funerals or going to church. Four research assistants, who are all health sciences students, were recruited to help collect the data.χ2) test in cross-tabulation was performed to determine the association between demographic variables and survey responses. Specifically, the researchers performed chi-square (χ2) tests to establish the relationship between the demographics  and COVID-19 prevention measures .The Statistical Package for the Social Sciences (SPSS) version 22  was used to capture raw data. Descriptive statistics in form of frequency analysis was performed to summarise the data in tables and figures. The chi-square (The study protocol was vetted and cleared by the Mzuzu University Research Ethics Committee (MZUNIREC), which is sanctioned by the National Commission for Science and Technology, which is a government agency mandated to oversee research activities, including ethics clearance and enforcement, in Malawi. The ethical certificate number is MZUNIREC/DOR/20/03. All participants signed informed consent forms and had the right to withdraw from the study at any time if they so wished.Of the 263 participants enrolled in the study, 131 (49.8%) were from Rumphi and 132 (50.2%) from Nkhata Bay. There were more female (57.8%) than male participants (42.2%). The majority (54.4%) were in the age range of 18–29. More participants indicated living in a household size of less than five members (55.5%). Many participants (41.8%) had only a Primary School Certificate of Education (PSCE) as their highest qualification. The majority of the participants reported earning less than MK50 000.00 (83.3%). Additional details are presented in The associations between demographic variables and adherence to COVID-19 containment measures are presented in p < 0.01), whereby more participants (90.1%) from Rumphi were more likely to use masks than participants from Nkhata Bay (77.3%); use of sanitisers (p = 0.03), whereby more participants (32.1%) from Rumphi were more likely to use sanitisers than those from Nkhata Bay (20.5%); social distancing (p = 0.01), whereby more participants (65.4%) from Rumphi were more likely to practise social distancing than those from Nkhata Bay (42.4%); and going to church (p = 0.02), whereby 23.7% participants from Rumphi were more likely to stop going to church than those from Nkhata Bay (12.9%).At the district level, a significant association was found between the district and the following COVID-19 prevention measures: wearing masks (p = 0.05), whereby more men (32.4%) were more likely to use sanitisers than women (21.7%), and social distancing (p = 0.04), where more men (61.3%) were more likely to practise social distancing than women (48.7%).Gender was significantly associated with the use of sanitisers (p < 0.01), whereby those aged between 18 and 29 (37.1%) were more likely to avoid going to crowded places than those aged 30 and 49 (18.2%) and those above 50 (17.9%).Age was only associated with avoiding crowded places (p < 0.01) and washing hands with soap (p = 0.04).Household size was significantly associated with the dimensions of the use of sanitisers (p < 0.01); those with a JCE were more likely to use sanitisers than primary school dropouts (7.7%) (p < 0.01); and those with an MSCE (47.1%) were more likely to avoid crowded places than those with the lowest qualification, i.e. primary school dropouts (11.5%) (p < 0.01).Level of education affected three preventive measures significantly: Those with advanced qualifications such as a bachelor’s degree (100%) were more likely to wear masks than those with lower qualifications such as primary school dropouts (71.2%) (p < 0.01).It was further found those earning more money, such as up to MK500 000.00 (71.4%), were more likely to use sanitisers than those earning less than MK50 000.00 (22.4%) (p < 0.01), and more participants from Nkhata Bay (52.3%) than those from Rumphi (27.5%) were less knowledgeable of English and Chichewa languages (p < 0.01). We found that more females (59.9%) lacked access to the radio than men (46.8%) (p = 0.04). Older participants were more affected by fake news (p = 0.03), and those aged above 40 and 50 were more affected by culture than those aged between 18 and 39 (p = 0.07).p = 0.09); those with lower qualifications had less access to the radio (p = 0.02), and those with lower qualifications had less knowledge in English and Chichewa (p = 0.03). We found that participants with lower monthly incomes were more likely to lack these resources (p < 0.01). We found that household size was not significantly associated with any factor (p > 0.05).Participants with lower qualifications were more likely to be health illiterate than those with higher qualifications  and its stakeholders in the COVID-19 fight not to relax their efforts in Rumphi while upscaling these efforts in the Nkhata Bay district.The study revealed that wearing masks, using sanitisers, practising social distancing and going to church were associated with the district; it was found that participants from the Rumphi district were more likely to follow these COVID-19 preventive measures than those from the Nkhata Bay district. This could be attributed to the level of intensity of civic education carried out by the two district hospitals – it might happen that Rumphi Hospital is more vigorous in its COVID-19 awareness campaign than Nkhata Bay. Awareness of the dangers of COVID-19 and its preventive measures has proven to reduce the transmission of the pandemic in Uganda.16 A report commissioned by the World Bank indicates that in lower-income countries, women are largely engaged in informal work,17 implying they are economically disadvantaged and find it difficult to access COVID-19 personal protective equipment (PPE); hence, it is not surprising that they could not access sanitisers. There have been anecdotal reports in Malawi that more men die of COVID-19 than women, and this information may have influenced more men to follow preventive measures than women. Much as there is no scientific evidence in Malawi, the literature suggests that across countries and age groups, once infected with COVID-19, men are more likely to die from or with COVID-19 than women.18 Because it was found that gender affects the COVD-19 fight, the health stakeholders need to take gender into account by ensuring that women and girls are included in education, decision-making and implementation initiatives.It was further found that gender (in favour of men) significantly affected the use of sanitisers and social distancing. In Malawi, men are the source of income; hence, they are more likely to find the money for buying sanitisers than women, who are mostly at the receiving end of all economic misfortunes. These results are consistent with those of a study in Germany which showed that the COVID-19 pandemic affected women more heavily than men in various ways which, among others, were a result of the gender economic gap in favour of men.20 these results suggest that there are greater risks resulting from the pandemic that may escalate in these communities. These findings suggest the need for community health education tailored to address the needs of specific groups.Younger people were more likely to avoid crowded places than older people. This can be attributed to the fact that older people are generally involved in fetching basic needs for their families; hence, they are more likely to be away from their homes. These basic needs are mostly accessible in places such as markets; hence, it becomes difficult for them to avoid such crowded places. Considering that older people are more likely to die of COVID-19 than young people,21Education was found to be key in the implementation of some COVID-19 prevention measures because the more educated the participants, the more they were likely to follow some containment measures, mainly wearing masks, using sanitisers, avoiding crowded places and practising social distancing. This is not surprising because it is common knowledge that the more educated the people, the more they are likely to understand and grasp issues, and COVID-19 issues may not be exceptional. Similar findings were reported in Iran that educated groups were more likely to follow COVID-19 preventive measures than those with lower educational levels.23 For example, De Souza et al.24 report a positive association between higher per-capita income and the COVID-19 fight in Brazil.According to the findings, the economic status of participants is key in the fight against the pandemic, because according to the study findings, the more money participants earned per month, the more they were able to use sanitisers. Unfortunately, the study findings reveal that the majority live below the poverty line and could not afford to buy PPE such as sanitisers and face masks. The impact on the economy of the COVID-19 fight has been also reported elsewhere in the world.25 and health authorities mostly use these when communicating COVID-19 messages to Malawians. The unfortunate part is that people in these districts speak their local or mother languages, which are Chitumbuka and Chitonga for Rumphi and Nkhata Bay, respectively. They only learn English and Chichewa in schools, implying that those who have not been to school cannot understand, write or read these languages. According to Thompson et al.,26 such citizens must rely on other citizens to be accurately informed, yet this process may lead to distortion of the meaning. In China, before the COVID-19 outbreak, Putonghua was a supreme language that was key for social inclusion,28 but noting that the language proved to be a barrier to the uptake of and compliance with public health directives, the government resorted to the adoption and enforcement of a multilingual approach.29 Therefore, streamlining and repackaging COVID-19 messages into local languages spoken in each district will be a more effective way of ensuring that the messages achieve the intended purpose. Such a strategy has worked in some African countries such as Ghana, where the use of songs with lyrics in local languages resulted in more uptake and actual practice of COVID-19 prevention by the people.26 The multilingual nature of Malawi, with more than 15 indigenous languages,25 implies that community radio stations are better suited to transmit such messages, unlike national radio stations which mostly produce their content in Chichewa and English.The results further revealed a significant association between the participants’ district of residence and health literacy on COVID-19 – participants from the Nkhata Bay district were more likely to be affected by lack of health literacy than those from the Rumphi district. The results suggest that people in Rumphi are more exposed to health messages on COVID-19 than those in Nkhata Bay. There is a need to amplify the COVID-19 awareness education in Nkhata Bay while ensuring that they do not relax in Rumphi. Relatedly, the results established that the district of residence was significantly associated with knowledge of English and Chichewa languages. People in Nkhata Bay had poorer knowledge of English and Chichewa languages than those in the Rumphi district. English and Chichewa languages are the official and national languages, respectively, for Malawi,30 suggest targeted health promotion and prevention awareness, which must be directed to the vulnerable individuals, in this case women, whom the study has proven to be more prone to COVID-19 fake news and misinformation. Fake news and misinformation in this context refer to false or unverified information about COVID-19 which is intentionally or unintentionally spread by community members.The present study found that gender was significantly associated with a lack of access to the radio: men were more likely to access the radio than women. This could be attributable to the fact that in these two districts and other parts of Malawi, men are traditionally the heads of the family and control most of the household property, including the radio; hence, the higher number of men accessing the radio according to the results is not surprising. Failure to access the radio, where most credible messages about COVID-19 are delivered, may have contributed to more women than men being exposed to fake news and misinformation on COVID-19. Iradukun et al.11 The present study confirms the results from Borg10 and Gaygısız et al.31 that culture has the potential to derail the efforts towards controlling infection of pandemics. The key custodians of Malawian culture are traditional leaders, with those at the pinnacle of the hierarchy called traditional authorities (TAs), who wield substantial influence over their subordinates and subjects. There is thus the need to engage these traditional leaders on the need to temporarily adjust some cultural practices that undermine the fight against COVID-19.Results revealed that older people were likely to disregard some COVID-19 containment measures because of their cultural traditions. In Malawi, older people are regarded as the custodians of cultural practices which are passed from one generation to another. In most cultures in Malawi, older people failing to patronise ceremonies attached to community culture, such as funerals, are reprimanded by the communities, and punishments include being disrespected by the community. Some of these cultural activities can fuel the spread of COVID-19 because they undermine some COVID-19 containment measures such as social distancing. Moreover, commonly, culture is a sensitive issue which health stakeholders need to approach with care when communicating to the concerned communities about its role in fuelling the pandemic. Elsewhere, when it enforced the wearing of masks and social distancing to control the pandemic, the American government faced resistance and backlash from its citizens, who expressed concern, saying their freedom was being violated.34 As shown in this study, lack of health literacy contributes to people falling victim to fake news, misinformation and myths about COVID-19 which are detrimental to the fight against the pandemic. It is perhaps for this reason that Habersa et al.35 emphasise the need to anticipate and manage misinformation by engaging with various media outlets. A report commissioned by the MoH revealed that most Malawians trust health personnel as sources of accurate information on COVID-19.36 Unfortunately, it is common knowledge that there are few health personnel in rural areas, and they cannot adequately serve the catchment areas, which are far and wide. More worrying is that the Health Surveillance Assistants (HSAs), who are at the periphery of the health system hierarchy in Malawi, are not adequately educated, as characterised by their basic informal training in health. The current study’s results are consistent with those of McCaffery et al.,37 who found that people who lacked health literacy in Australia demonstrated poor understanding of COVID-19 prevention measures and were more prone to COVID-19 misinformation and fake news.The results revealed a significant association between the level of education and health literacy on COVID-19. Health literacy entails a combined set of skills that enable people to access, understand and implement health advice and make informed health decisions.p < 0.01) was found between monthly income and lack of resources for following COVID-19 containment measures. Fighting COVID-19 requires resources. People need PPE such as face masks, hand sanitisers and well-ventilated houses. Even those containment measures that may not directly need money are related to economic issues. For instance, people may not fully follow social distancing because places such as communal markets cannot be avoided because they are the hubs of community economies, thereby leaving socio-economically disadvantaged people more susceptible to the pandemic. Moreover, some previous studies have shown that social distancing is more difficult in socio-economically disadvantaged neighbourhoods where crowding results in individuals or communities to be more likely to be exposed to COVID-19.41 With most participants living below the international poverty line of $1.90 per day,42 the fight against the pandemic becomes more complex and problematic. The impact of the economy on the COVID-19 fight has been also reported elsewhere in the world.24 For example, De Souza et al.24 similarly found a positive association between higher per capita income and COVID-19 diagnosis in Brazil. The present study’s findings are further consistent with those reported in Hong Kong, where during the third wave of the pandemic, as the government enforced more stringent measures, the socio-economically disadvantaged people and neighbourhoods were more affected.9 A study commissioned by the United Nations Development Programme12 noted that the high poverty rates in Malawi made people more concerned about finding food than being concerned with contracting the virus. The present study’s authors therefore agree with some previous studies that concluded that to effectively contain COVID-19 transmission, policymakers need to invest more resources in these economically disadvantaged individuals and communities.31A strong association (Participants were recruited from rural areas, and the results may not be appropriately applicable to urban populations. More so, with data collected from two districts in the Northern region, the findings may not be adequately generalised to the population of other regions of Malawi – there are diverse factors across regions, districts and communities such as culture, economy, etc.Coronavirus disease 2019 has not spared any single place, and this study finds that people living in rural communities are socially and economically disadvantaged, making them highly vulnerable to COVID-19 infection. Disparities were found in their knowledge of the language, level of education, culture, monthly income and health literacy which compel the researchers to question the deployment of unified COVID-19 containment strategies. To this end, the authors propose that health authorities should repackage the COVID-19 containment measures, prioritising and tailoring them to meet the needs of these groups. In particular, the low levels of income of the rural people and culture are some of the key factors forcing the people to neglect COVID-19 containment measures; the authors therefore propose that the government should consider extending the Emergency Cash Transfer Programme to rural people. With the Malawian economy suffering a massive nosedive resulting from the effects of COVID-19, there is a need to adopt all possible strategies that will help control the pandemic. The researchers noted that the economy frustrates rural people from adhering to some COVID-19 containment measures such as the use of sanitisers. Encouraging rural dwellers to follow COVID-19 containment measures that have little or no economic ramifications could be a more appealing strategy. Like other previous studies, this study found that gender influences adherence to some COVID-19 containment measures; hence, the authors call upon health stakeholders to include women and girls in COVID-19 interventions. The researchers are optimistic that their findings have implications for controlling current and future communicable diseases – the strategies they have proposed might be employed in fighting such pandemics.