Monitoring coronavirus disease (COVID-19)-related infections and deaths inBrazil is controversial, with increasing pressure to ease social distancemeasures. However, no evidence of a sustained, widespread fall in casesexists.  We used segmented (joinpoint) regression analysis to describe the behaviorof COVID-19 infections in Brazilian capital cities.  All capitals showed an exponential or a near-exponential increase in casesthrough May. A decline in reported cases was subsequently noted in 20 citiesbut was only significant for 8 (29.6%) and was followed in two by a renewedincrease.  Caution is warranted when considering the relaxation of restrictions. In December 2019, China released the first report of coronavirus disease (COVID-19)The Brazilian Ministry of Health confirmed that the first case of COVID-19 in LatinAmerica occurred in Brazil in late FebruaryAlmost four months since the first confirmed case in Brazil, the number of cases anddeaths continues to increase. At the subnational level, some areas are stillexperiencing the initial trend of exponential growth, and some local health systems andfuneral services have reached their thresholdBrazil is rife with public disagreements about social distancing and the severity of thequarantine measures adopted by most state governors. The federal government faces aninstitutional crisis regarding the necessary steps to be taken to protect thepopulation. Simultaneously, the accuracy of the criteria that define COVID-19 cases anddeaths is being questioned in the parliamentMeanwhile, some states are planning to relax their quarantine rules despite the collapsesexperienced by some local health systems. The main argument fueling this decision isthat a recent change in the transmission curve has been observed, with a tendency towardstability and decline. Thus, the objective of this study is to describe the behavior ofthe historical series of COVID-19 cases in Brazilian state capitals and identify changesin the trend over the course of the epidemic.We performed the analysis using case notification data for Brazilian capitals, stratifiedas epidemiological week. Data were obtained from the Secretaries of Health of each ofBrazil’s 26 states and the national capital of Brasília. To assess whether the trendschanged over time, we used the Joinpoint segmented regressionth epidemiological week (ending on July 4). We chose to perform theanalysis using capitals rather than using federation units (states) to ensure datastability as well as to minimize the effect of inner state heterogeneity. To avoidautocorrelation between the dependent and independent terms of the regression equation,we used a “week-centered” variable as the time unit. Moreover, to guarantee theassumption of homoscedasticity, we used Poisson distribution parameters with robustvariance. In this way, we adjusted the regression by considering the number of casesweekly as the dependent variable and the centered week as the independent variable. Theselection of the number of inflection points was performed automatically by thesoftware. We considered the level of significance at 5%.We used epidemiological weeks as the time unit, from the first 2020 epidemiological weekto the 27nd week(May 24th to May 30th). The full pattern of the growth curves, however, varied in eachcapital. A distinct drop was detected in 20 of the 27 cities (74.1%) and was significantin 8 . However, in most cities, the speed (slope) of the decline was much lower thanthat of the increasing rate before the curve’s peak. We can check that looking at thecurves . Finally, it is essential to note that BeloHorizonte and Salvador experienced a renewed increase after a short period of decline most likely between two and fourThe rapid increase in COVID-19 clinical cases, characterized by the exponential patternof the accumulated case curve, suggests that its transmissibility is high, with thebasic reproductive number (RUnderstanding the stage of the epidemic we are currently in is essential for predictingscenariosThe marked difference between the capitals may reflect differences in the measuresadopted and the adherence of the population to those measures. The rate of decline inthe curves is less than the rate of increase at the beginning of the epidemic. Anyspeculation regarding this decline and the use of the information to justify theflexibility and accelerated opening of public activities thus requires caution. Thecases of Belo Horizonte and Salvador can be considered, wherein the rate of newinfections declined, followed by an increasing rate higher than that in the first phasein short period. Finally, the decreasing trends in the capitals may be an indication ofthe epidemic’s migration to inland cities, The health service network will have to paycareful attention, as most likely, people who have contracted the infection will travelto the capitals for treatment, and without social distancing policies, thousands ofsusceptible people will be exposed to these cases.Modeling is limited by the availability and quality of data, which in our case was theunderreporting of new infections. Lack of tests is a barrier to establish the realnumber of people infected. The actual number of COVID-19 cases is undoubtedly muchhigher. Additionally, access to screening tests is different between the capitalsAlfano & ErconaloThe general recommendation is that the social distancing measures should be graduallyeased in regions where the number of cases is few and an efficient public health systemthat can meet medium and high complexity medical demands such as intensive care unitsexists. Furthermore, before relaxing these guidelines, the country’s testing capacityneeds to be substantially increased, considering Brazil’s continental dimensions andregional differences, including demographics, climate, urbanization, health carestructure, and socioeconomic aspects. The joinpoint analysis only offers a description of the time series, and we did notintend to establish a firm conclusion on social distancing measures based solely ontemporal changes. In fact, the results indicate that it is too early to make radicaldecisions on returning to pre-lockdown activities involving potentially large groups ofpeople. Owing to numerous uncertainties, we consider it helpful to be cautious aboutreturning to activities that would result in several people on the streets. Given thecurrent situation, it would be wise to propose that the social distancing measure remainfirm, despite their economic and social repercussions. Lastly, the current phase of theepidemic requires special attention to be paid to the needs of medium- and small-sizedcities.


Breast cancer is the most common cancer spread among women worldwide. Whereas many studies have discussed the significance of breast cancer screening among women in various countries, few have attempted to discuss this topic among female school teachers. As teachers educate and communicate with students, this may play an essential role in health education and in promoting healthy behavior, such as breast cancer screening. The primary goal of this study is to develop and implement an educational intervention of breast cancer screening and evaluate its effectiveness among Yemeni female school teachers in Malaysia. This was carried out as per the health belief model. A cluster-randomized controlled trial was conducted among 183 Yemeni female school teachers in twelve schools in Klang Valley, Malaysia. A random assignment of the target schools was made to include them within the intervention or control group. Participants in the intervention group were offered a 90-min session for one-day educational intervention on breast cancer screening. On the other hand, participants in the control group were offered the same educational materials at the end of the study. Relevant data was collected at baseline, one month following the intervention, and then three- and six-months follow-up assessments. Analysis of such data was done via IBM SPSS software 25.0 by generalized estimating equations (GEE) to assess the differential changes over time. A primary outcome embodied in breast cancer screening practice uptake was expected. Secondary outcomes include the target group’s knowledge on and beliefs of breast cancer screening. This study intends to contribute to the credibility and effectiveness of utilizing a theory-based breast cancer screening intervention in order to raise the awareness of women on conducting breast cancer screening. Although efforts and extensive progress have been made in medical treatment, breast cancer (BC) continues to be among the highest life-threatening diseases among women . It is aPractically speaking, BC early detection examinations help save thousands of lives each year and improve the chances for early detection of BC, and therefore successful treatment . SeveralTheoretically, different models and theories have been used in order to comprehend BC early detection. One is the health belief model (HBM) that is used as the theoretical framework in numerous educational interventions. HBM has proven to be effective in improving factors impacting BCS behaviors ,12,13,14Many studies on BCS performance have been implemented among women worldwide. Such studies have incorporated certain women groups such as female university students, female workers, women attending health care centers, or female residents ,18,19,20In brief, interventions focusing on BC early detection are less common among school teachers, in general, and absent among Yemeni school teachers in Malaysia, in particular. Therefore, not only does the current study intend to develop and evaluate an educational intervention based on the HBM but also to educate Yemeni female school teachers in Arabic schools in Malaysia and provide them with knowledge and enhance their beliefs of BC and BCS practice. It also aims to promote BCS among them.i.An increase in the proportion of their BCS uptakeii.An increase in their BC knowledge scoresiii.An improvement in their BC health beliefs .The overall objective of this study is to develop and evaluate the effectiveness of an educational intervention grounded in the HBM on BCS uptake , knowledge, and beliefs among Yemeni female school teachers in Klang Valley, Malaysia. It is hypothesized that starting from baseline until the three- and six-month assessment, women in the experimental group will gain the following: This is a parallel cluster-randomized controlled trial (cRCT), with schools being the unit of randomization (clusters). The trial will be used to assess the effectiveness of an educational intervention on BCS uptake, knowledge, and beliefs of Yemeni female school teachers at both elementary and high Arabic schools in Klang Valley, Malaysia. There are nineteen well-known Arabic schools in Klang Valley area, with teachers from different Arab countries . However, the main composition of the teaching bodies is Yemeni teachers, as six of the schools are Yemeni schools. Twelve of the schools were selected as clusters (schools), and randomly assigned for the intervention or the control group. While the intervention group was given an educational intervention on BCS, the control group received the educational materials on BCS intervention after the completion of the study. The inclusion criteria for schools will take into account the following requirements: (1) such samples are elementary and high Arabic schools; (2) they are situated in Klang Valley; (3); and they consent to participate in the study. The exclusion criteria will disqualify Arabic schools that do not have Yemeni female teachers from the study,The inclusion criteria for teachers takes into consideration the following requirements: (1) such participants must only be Yemeni female school teachers teaching at the selected elementary and high Arabic schools situated in Klang Valley; (2) participants are aged 20 years old or older; (3) they sign a consent form to take part in the study. The exclusion criteria for teachers include (1) teachers who are retiring during the study, (2) teachers who have been diagnosed with BC, and (3) teachers who are lactating or pregnant.The intervention group will be introduced to an educational intervention on BCS. This intervention is grounded in the HBM and developed based on the American Cancer Society , AmericaThe educational intervention consists of four units. Unit One provides general information on the anatomy and physiology of a normal breast in order for the participants to have a clear understanding of the topic. Unit Two gives information and knowledge of BC. It further explores BC symptoms, BC stages, BC risk factors in order to increase the participants’ knowledge of BC. Unit Three offers an explanation of two different methods of BCS (CBE and mammography) in a bid to encourage participants to adopt and practice these approaches. Unit Four explains the BSE procedure to raise the participants’ awareness of BC symptoms and motivate them to follow this procedure. Following the development of the educational intervention, face and content validity were checked and assessed by five professional expert panelists from the Community Health Department and Cancer Resource and Education Centre at the Faculty of Medicine and Health Sciences, Universiti Putra Malaysia. The experts approved the educational material as efficient with respect to the study objectives. Their feedback focused on the understandability and simplicity of the content. They suggested that information about the incidence of BC and any medical terms from the module ought to be removed because it is irrelevant to the layperson, a glossary of terms for the module ought to be drafted, and colorful pictures for the module should be included in order for the module to be clearer and more interesting. (a)A 60-min PowerPoint presentation will be delivered along with a revised five-minute short video about BSE performance provided by the permission of Nuffield Health . Another(b)A thirty-minute training session on BSE practice on a silicone breast model with several implanted lumps will be carried out. In this session, participants will acquire knowledge on palpation technique and search strategy as well as signs of BC in order to be aware of when examining the breast. Following that, participants will be asked to perform BSE based on what they have learnt.(c)At the end of the educational intervention, participants will be offered a copy of the relevant booklet containing all the information delivered to them in the educational intervention; they will be given CDs of the five-minute short video about BSE performance, and a BC logo sticker to be hung on the participants’ mirrors. The significance of distributing such materials lies in reminding the participants of the high importance of BCS practice. Along with encouraging the performance of BSE on a monthly basis, this aims at enhancing the intended messages.(d)All participants in the intervention group will continue to receive short reminder text messages for a period of 6 months. The text message will read as: “This is a gentle reminder of BSE monthly performance and BCS practice.” The key messages will provide recommendations about BCS behaviors to remind, motivate, and encourage participants to practice BCS behaviors.The educational intervention involves a 90-min session for one-day. The study researcher with the help of a trained research assistant, will conduct the session for the intervention group at each respective school. It is expected that three sessions will be carried out each week. Although the implementation date of the intervention may differ from one school to another, its duration time will be the same for all. The following is a breakdown of the procedures to be conducted:Furthermore, participants in the control group will not receive any education during the study period. However, they will be given the same educational materials on BCS at the end of the study, and they also will answer the same sets of the questionnaires at baseline, one month following the intervention, then after three- and six-months have gone by after the intervention. To support the fidelity of the educational intervention, some strategies are used. These include the strategy for preserving the consistency of the intervention delivery and for encouraging participants’ adherence to the intervention and reinforcing such abidance on their part. To maintain consistency in the intervention delivery, the educational intervention is implemented independently in each study cluster by the same researcher. The researcher will be committed to adopt a single educational intervention protocol to ensure a standard delivery across the respective schools. BSE training will be planned and rehearsed by the researcher by using a role-play technique with the participants. The researcher acts as an observer during the sessions and will provide immediate feedback to the participants. Other possible means enhancing intervention fidelity include the monthly short reminder text messages and the use of the BC logo sticker. These are mainly employed to encourage participants, remind them of the importance of BCS practice and monthly BSE performance, and to reinforce the intended messages.To ensure sincere responses, honesty will be emphasized to the participants. They will be informed that all questionnaires will be anonymous with a unique number to identify participants later on. The primary outcome variables in the present study includeBSE practice and performance frequency will be measured using a self-report answer instrument. Developed by Akhtari-Zavare et al. , this inA woman who does BSE once a month will be classified as practicing (regular BSE); any other answer will be classified as non-practicing (irregular BSE). The proficiency of performing BSE will be measured by asking the participant to perform BSE on a breast model through BSE proficiency rating instrument (BSEPRI) . This inScreening practice regarding CBE and mammography will be assessed using self-report questions assessing participants as “practicing” or “not practicing”.Using self-reporting in some of the primary outcomes may lead to some sort of bias judgment. Although unavoidable, the researcher tries to reduce this prejudice by being more objective at the data collection stage. This is by using simply precise language, maintaining a short time frame, and assuring confidentiality and anonymity of participants.The secondary outcome variables in this study includeKnowledge of BC and BCS methods will be measured using a modified questionnaire consisting of items adapted from Parsa , AkhtariHealth beliefs pertaining to BC will be assessed by the use of a modified version of the CHBMS Champion . The queThis part of the questionnaire consists of five questions conducive for assessing the socio-demographic factors of the participants . In addition, there is another question on previous reading or hearing about BCS.Lawshe’s method  was usedThe questionnaire and study module were translated according to the process of professional translation, while the back translation was used as an approach for the adaptation of instruments . The admLater on, the translated module and questionnaire were pre-tested among 30 Yemeni female school teachers, who are not of the participants in the study. The clarity, understandability, and quality of the module and questionnaire were discussed with them individually. Then, phrases that were unsuitable and/ or difficult to understand were identified, and accordingly, adjustments and modifications thereto were made following their evaluation. Subsequently, the final version was approved for the study.For the sake of calculating the appropriate sample size for clustering, the standard sample size estimate for the individually randomized design will be calculated. This calculation process is done first by applying the formula to find the difference between two population proportions , . Then iA total number of 12 schools with 183 participants that have met the inclusion and exclusion criteria will take part in the study. Following cluster sampling, six of the schools will be involved in the intervention group, while the other six are the control group. Then the proportionate allocation number of teachers to participate in the study will be calculated based on the density of the teachers in each school . Participants’ selection will be conducted by using a simple random sampling technique.Nineteen Arabic schools with 289 Yemeni teachers were assessed for eligibility. Seven schools with 39 teachers were not eligible to take part in the study. In other words, whereas three schools did not show any will and interest to take part in the study, the remaining four schools did not meet the inclusion criteria as they did not include any Yemeni teachers.With respect to the 12 eligible schools (12 schools), they will be invited to contribute to the study. Written consent will be obtained from respective authorities in all the schools. With the help of school coordinators, eligible teachers would be invited to attend small meeting groups in each school. During such meetings, the researcher will explain the objectives and advantages of the study, along with the inclusion and exclusion criteria. After selecting the required participants’ number in each school, participants will be asked for written informed consent. Accordingly, participants who agree to participate will be asked to fill in the questionnaire. Later on, educational interventions on BCS will be delivered to the intervention group. Following that, the participants will be requested to fill in the same set of the questionnaires  in the post and follow-up assessment. The reporting of this protocol followed the Standard Protocol Items: Recommendations for Interventional Trials (SPIRIT) 2013 Statement . The stuThe unit of randomization in this current study is a school. To allocate the twelve eligible clusters (schools) to the study groups, an independent statistician will create the randomization sequence using the Sealed Envelope tool , with a To ensure proper allocation concealment, a statistician will be assigned to produce the allocation sequence list by using block randomization software. Each cluster will be given a unique code in a sealed opaque envelope. Following that, the research assistant will open the envelopes and assign the clusters to the intervention or control group based on the list of codes generated by the software. Moreover, to avoid bias at the individual level, the envelopes will be opened only after the school coordinator selects the required participant number, and after obtaining consent for treatment and collecting baseline data.t-test will be applied to compare the mean difference of continuous data of the intervention with that of the control group. IBM SPSS Software 25.0  will be used to analyze collected data. However, missing data will be managed by adopting the intention-to-treat analysis concept. Prior to analysis, continuous variables will be checked for normality. The alpha level of significance will be set at the value of <0.05. Descriptive analysis will be employed to describe the data at baseline. Regarding the between-group comparisons, the chi-square test will be applied to compare the frequency difference of categorical data of the intervention with that of the control groups. The independent-samples Furthermore, to evaluate the changes within both groups, Cochran’s Q test will be applied to test the frequency differences of categorical data within the study groups’ overtimes. The one-way repeated measures ANOVA will be applied to identify the mean difference within the study groups for continuous variables overtimes. Generalized estimating equations will be used to test the main effect and interaction between and within the overtimes of the intervention and control groups.Ethical clearance was obtained from Jawatankuasa Etika Universiti Untuk Penyelidikan Melibatkan Manusia (JKEUPM) at Universiti Putra Malaysia [Ref No. FPSK(EXP16)P151] and the participating schools. This study will follow the ethical criteria throughout its entire procedure. Participants will be assured that their participation in the study is voluntary, and that they have the full right to withdraw from the study at any time they please. A participant consent form will be signed by each participant prior to conducting the survey. Data will be held in a confidential and anonymous manner. This study was registered at ACTRN: ACTRN12618000173291).This study presents a protocol aimed at examining the impact of BCS educational intervention on the BCS practice uptake, knowledge, and health beliefs of Yemeni female school teachers in Malaysia. It is believed that late-stage BC imposes extremely serious burdens on patients and their families. It further causes economic burdens on employers, communities, and governments. Our educational program in this regard serves as a plan in BC prevention through engaging an underserved population. The aim is to improve BCS outcomes and reduce disparities. Accordingly, this study intends to contribute to the credibility and effectiveness of utilizing a theory-based intervention aiming at educating women about BCS. The expected findings offer a better understanding of the role of knowledge and beliefs in promoting BCS uptake. Not only do the findings directly benefit teachers, their students, families, and friends, but also inform researchers and health care workers who strive to produce interventions serving women. The knowledge gained from this study could be used to plan appropriate BCS programs for other women in different contexts. These programs strengthen health systems with the intention to widely spread appropriate knowledge in communities. They could be utilized to ensure awareness of targeted groups at both the individual and social levels. To the best of the researchers’ knowledge, this study is the first of its kind that evaluates BCS educational intervention using a theoretical framework among Yemeni school teachers in Malaysia. If proven effective, this educational intervention will be evaluated and implemented in a diversity of settings that provide health education interventions for women. Though there is no proof that BSEs reduces BC mortality rates, it should be encouraged to detect BC in women efficiently. Moreover, although BSE may be an essential way to raise breast awareness, women are still asked to bear the responsibility for their own health by examining themselves. This is in order for them to be familiar with their breasts, observe and feel any changes from the norm, and consequently report any apparent changes immediately. Along with raising women’s awareness to undergo mammography, BSE may also involve the first stage in decreasing the barriers preventing women from undergoing CBE and mammography. In addition, CBE might create an opportunity to discuss screening matters with physicians. Thus, proper educational interventions are required to urge women to adhere to regular breast awareness and BCS examinations.To recapitulate, this study examines the effects of BCS educational intervention on the BCS practice uptake, knowledge, and health beliefs of Yemeni female school teachers in Klang Valley, Malaysia using a cRCT design. The current study has a twofold aim. On the one hand, it aims to develop an educational intervention grounded in the HBM on BCS uptake, knowledge, and beliefs. On the other hand, it aims to assess the effectiveness of the educational intervention on BCS uptake, knowledge, and beliefs among Yemeni female school teachers in Klang Valley, Malaysia, in different time points . In other words, since teachers play a vital role in educating and motivating young generations and future societies, it is highly important to provide them better health education.


Malaria infects and kills millions of people in Africa, predominantly in hot regions where temperatures during the day and night are typically high. In South Africa, Limpopo Province is the hottest province in the country and therefore prone to malaria incidence. The districts of Vhembe, Mopani and Sekhukhune are the hottest districts in the province. Malaria cases in these districts are common and malaria is among the leading causes of illness and deaths in these districts. Factors contributing to malaria incidence in Limpopo Province have not been deeply investigated, aside from the general knowledge that the province is the hottest in South Africa. Bayesian and classical methods of estimation have been applied and compared on the effect of climatic factors on malaria incidence. Credible and confidence intervals from a negative binomial model estimated via Bayesian estimation and maximum likelihood estimation, respectively, were utilized in the comparison process. Overall assumptions underpinning each method were given. The Bayesian method appeared more robust than the classical method in analysing malaria incidence in Limpopo Province. The classical method identified rainfall and temperature during the night to be significant predictors of malaria incidence in Mopani, Vhembe and Waterberg districts. However, the Bayesian method found rainfall, normalised difference vegetation index, elevation, temperatures during the day and night to be the significant predictors of malaria incidence in Mopani, Sekhukhune and Vhembe districts of Limpopo Province. Both methods affirmed that Vhembe district is more susceptible to malaria incidence, followed by Mopani district. We recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria control, prevention and elimination to Vhembe and Mopani districts of Limpopo Province. Plasmodium Falciparum, Plasmodium vivax, Plasmodium malariae and related species of Plasmodium ovale and Plasmodium knowlesi ([Anopheles mosquitos (mosquitos carrying protozoa). Plasmodium falciparum is known to account for many malaria cases globally and is therefore regarded as a threat to public health worldwide . Mala. MalaPlaThe symptoms of malaria include fever >37.5 °C), headache, rigors, muscle pains, diarrhea, nausea, vomiting, loss of appetite, inability to feed babies, dizziness and sore throat. Based on history, malaria has infected and taken the lives of millions of individuals. This disease remains a major cause of human morbidity and mortality in most of the developing countries in Africa. Young children, pregnant women, and elderly individuals are groups of people at higher risk of malaria transmission  models have been applied. For instance, Shimaponda-Mataa et al. ,13,14,15Abiodun et al.  conducteAnother point of departure is that many of the data sets employed in most of these previous studies in one way or the other have used environmental factors and other predictors of malaria. Environmental factors such as elevation, temperature, normalised difference vegetation index (NDVI) are dynamic and their effect on malaria incidence cannot be fixed, although they are unknown as claimed by the classical approach. A Bayesian estimation therefore will be adopted in the present study to model these factors on malaria incidence because it considers these environmental factors as random variables with some probability distribution. This has made the use of Bayesian estimation methods very popular. In classical paradigm, parameters of a model are unknown, but fixed constants, while in Bayesian estimation the parameters are random, with knowledge about the parameters described in the form of a probability distribution. We seek to explore these two methods and compare them in the context of malaria incidence in Limpopo Province of South Africa. This is limited in a number of studies conducted on malaria incidence, especially in Southern Africa.In this paper, classical and Bayesian estimation methods will be utilised and compared in the context of modelling malaria incidence. The relation between the two statistical estimations are from the fact that the posterior distribution in the Bayesian approach is proportional to the likelihood function times the prior distribution. Whereas maximum likelihood estimation (MLE) uses asymptotic distributional assumptions in classical statistics, the uncertainty about model parameters in the Bayesian approach is expressed through the prior distributions. Combining the prior distribution and likelihood (data), researchers are able to update the knowledge about the model parameters. This is done through the posterior distribution from which we can infer the estimates of the model parameters and relevant quantities like credible intervals.The rest of the paper is outlined as follows: This study models malaria incidence in Limpopo Province of South Africa. Limpopo Province consists of five districts: Capricorn, Mopani, Sekhukhune, Vhembe, and Waterberg. Malaria incidence or cases data were provided by Malaria Control Center located in Tzaneen. The population data were provided by Statistics South Africa (StatsSA) . EnvironThe Poisson distribution is probably the most used discrete distribution because of its simplicity. The Poisson probability mass function is given by:The mean and variance of the Poisson distribution are equal to Consequently, the Poisson regression model is:From (3), the mean and variance of the Poisson regression model are equal to The use of negative binomial (NB) model in count data modelling, as it is the case in this study, often comes up when there is over-dispersion. While the Poisson distribution is often first to be considered for fitting count data such as malaria incidence, nevertheless, if the mean is very much less than the variance of the data, then there is over-dispersion in the data. An alternative approach to correct over-dispersion is to fit a NB regression model to the over-dispersed Poisson regression model. The probability mass function for the NB distribution is given by:The mean and variance of the above model are respectively given by The mean and variance of the NB regression model are correspondingly given by to zero .Assume we observe Taking the log of (6) gives the log-likelihood function as:Similarly, the log-likelihood function for NB regression model can also be estimated via maximum likelihood. Cameron and Trivedi  gives thA common goodness-of-fit statistic for count regression models is the Pearson’s posterior probability, of an event A after B as observed in terms of the prior probability of A, prior probability of B, and the conditional probability of B given A. The foundation for Bayesian inference is defined from Bayes theorem and is given as follows:Bayesian statistics to a large extent can be attributed to Reverend Thomas Bayes (1701–1761), who developed Bayes’ theorem. Bayes’ theorem expresses the conditional probability, or y, By substituting The presence of the marginal distribution likelihood of Through the Markov chain Monte Carlo (MCMC), we use a Gibbs sampler for the NB regression model. This model is derived from the Poisson regression model to account for over-dispersion, which usually happens or occurs in count data. Suppose the response are independent, then:The conditional likelihood of for for update update The conjugate prior full conditional distribution for This section presents and discusses the results for fitting count regression models, namely; the Poisson regression model and NB model estimated with maximum likelihood, and Bayesian estimation methods. The variables used in fitting both models are described in The histogram displayed in According to As shown in p-values for all the covariates as displayed in The The Pearson’s chi-square is considered to be robust in detecting over-dispersion in Poisson models. If the ratio of the residual deviance and the degree of freedom is significantly larger than 1, then the probability that the developed model is over-dispersed is high. Based on the Poisson model, the ratio of the residual deviance and the degrees of freedom is 12.753. This implies that the probability that the selected Poisson model is over-dispersed is very high. To validate that the Poisson model selected may be over-dispersed, we check if the response variable satisfies the Poisson assumption of an equality between the mean and the variance. p-values for Mopani, Vhembe and Waterberg are very close to 0. These p-values implies that there is a certain pattern of malaria transmission between these districts and Capricorn district (the reference category). The coefficient estimates for Mopani, Vhembe and Waterberg are positive. These estimates entail that if malaria incidence increases in each of these districts, then it also increases in Capricorn district (the reference category). We are using the odds ratio, β in the odds ratio e2.215 ≈ 9 times in Mopani, e2.848 ≈ 17 times in Vhembe and e0.8711 ≈ 2 times in Waterberg than in Capricorn district. A unit increase in temperature during the night increases the incidence of malaria by e0.2537 ≈ 1 unit. There is no evidence of an existing association between malaria incidence and the covariates, temperature during the day, elevation and NDVI according to All the 95% credible intervals presented in This implies that there is a very high probability that the estimates of these regression coefficients are negative. Therefore, we can conclude that the relationship between malaria incidence and each of the covariates rain and elevation is negative. That is, an increase in rainfall leads to a decrease in malaria incidence and an increase in elevation above sea level leads to a decrease in malaria incidence.All the 95% HPD credible intervals for temperature during the night, temperature during the day and NDVI are positive, which indicate that there is a very high probability that the estimates of these regression coefficients are positive. Therefore, we can conclude that the relationship between each of these covariates and malaria incidence is positive. That is, an increase in temperature during the night, temperature during the day and NDVI results in an increase in malaria incidence.The 95% HPD credible intervals for Mopani, Sekhukhune and Vhembe districts are positive, which indicate that as malaria incidence increase in each of these districts, it also increases in Capricorn district (reference variable). However, both the 95% HPD credible intervals for Waterberg are negative. This implies that if malaria incidence increases in Capricorn district, then it decreases in Waterberg district. We can then conclude, according to the MCMC estimation methods applied to obtain the model in Both the Bayesian and classical methods revealed a positive relationship between malaria incidence and temperature during the night. That is, an increase in temperature during the night results in an increase in malaria incidence. Therefore, we can conclude that the risk of malaria transmission is high during warm nights, which are usually the nights of summer seasons. The Bayesian and classical frameworks produced similar results about the relationship between malaria incidence and rainfall, which was found to be negative. Therefore, we can conclude that an increase in the amount of rainfall results in a decrease in malaria incidence. The classical framework does not provide any evidence of an existing relationship between malaria incidence and either elevation, temperature during the day or NDVI. However, the Bayesian framework revealed that an increase in NDVI and temperature during the day lead to increased malaria incidence while an increase in elevation above sea level leads to decreased malaria incidence. Malaria incidence increases in Mopani and/or Vhembe districts, then it also increases in Capricorn district. The classical framework revealed no pattern of malaria incidence between Capricorn and Sekhukhune districts while the Bayesian framework suggests that if malaria incidence increases in Sekhukhune district, then it also increases in Capricorn district. The Bayesian framework also suggests that if malaria incidence decreases in Waterberg (though not significant) district then it increases in Capricorn district while in contrast, the classical framework suggest that if malaria incidence increases in Waterberg district then it also increases in Capricorn district. Both methods affirm that Vhembe district is more susceptible to malaria incidence, followed by Mopani district, confirming the results of Abiodun et al. ,8. The cThis study was limited to one South African province, Limpopo. However, there is another province, Mpumalanga which is also known to account for high malaria cases in South Africa. This study only made use of non-informative priors. However, future research may consider Bayesian estimation under dissimilar prior distributions such as improper, conjugate and Jeffrey’s priors in the model fitting process. The spatial dimension in the context of Bayesian estimation may be considered in analysing the prevalence of diseases such as malaria in future research.Based on the findings from this study, we recommend that the Department of Health and Malaria Control Programme of South Africa allocate more resources for malaria prevention, control and elimination to Vhembe and Mopani districts in Limpopo Province. We also recommend that the government provide educational seminars to educate the South African communities on how to prevent malaria transmission, especially during the warm summer nights.


As the crisis around Covid-19 evolves, it becomes clear that there are numerous negative side-effects of the lockdown strategies implemented by many countries. Currently, more evidence becomes available that the lockdowns may have more negative effects than positive effects. For instance, many measures taken in a lockdown aimed at protecting human life may compromise the immune system, and purpose in life, especially of vulnerable groups. This leads to the paradoxical situation of compromising the immune system and physical and mental health of many people, including the ones we aim to protect. Also, it is expected that hundreds of millions of people will die from hunger and postponed medical treatments. Other side effects include financial insecurity of billions of people, physical and mental health problems, and increased inequalities. The economic and health repercussions of the crisis will be falling disproportionately on young workers, low-income families and women, and thus exacerbate existing inequalities. As the virus outbreak and media coverage spread fear and anxiety, superstition, cognitive dissonance reduction and conspiracy theories are ways to find meaning and reduce anxiety. These behavioral aspects may play a role in the continuance of lockdown decisions. Based on theories regarding agnotology , social influence, superstition and stress and coping, I seek to explain the social and behavioral aspects of human behavior in times of crises. Both the Covid-19 crisis itself as well as the resulting economic and  health crisis are global problems that may require global solutions. I present a model of drivers and outcomes of lockdown behaviors and offer suggestions and a tool to counteract the negative psychological effects by means of online life crafting therapeutic writing interventions. In search of America (1962)1 and disease control, has led to many consequences that affect people’s mental and physical health and safety  judgment has been under investigation for decades. Three famous experiments show the intricacies of social influence, which have become known as the Asch conformity experiment, the Milgram obedience experiment, and the Stanford Prison experiment. In the Asch experiment, it was shown that even in a very unambiguous situation, with one clear right answer, 75% of people could be persuaded to give the wrong answer as long as the “stooges”, hired by the experimenter, also gave one clear but false answer . In thisAlthough in many instances conformity and obedience are functional, in these experiments and in some instances in real life, conformity and obedience can become dysfunctional and even harmful. A review by Stress occurs often when people face challenging or difficult situations  resulting in physiological and psychological responses (stress responses). One of bodily systems reacting to these stressors is our immune system. In acute stress the body reacts to stress with the increase of pro-inflammatory cytokines. The body is prepared for a fight or flight response. Acute stress in a healthy human is quite harmless, but stress that last for days, weeks, months or years can be harmful . It can The duration of the social and physical isolation is of importance. During the SARS outbreak people that were isolated for more than 10 days showed significantly higher post-traumatic stress syndrome than those who were isolated less than 10 days . In manyfunctional coping styles in order to offset some of these negative effects have been blocked in some ways due to the measures and this may have led to downward spirals in terms of  health  health . These i) health , as well) health . Seeking) health . This maDysfunctional coping strategies, such as withdrawal/ruminating, substance use, taking tranquilizers and excessive gaming can exacerbate the negative effects of the lockdown measures cf. , and it Sense making may be a third, hitherto unexplored way of coping. In uncertain times like these, people may try to cope by making sense of the situation e.g., . ScientiCognitive dissonance will create tension between the belief that the sacrifices people make are necessary and the belief that some of these behaviors may be causing more harm than good in terms of mental health . The unpCoping strategies such as cognitive dissonance reduction, superstitious beliefs and rituals, as well sense making through conspiracy theories, although somewhat functional in terms of reducing anxiety, are not satisfying key psychological needs in the long run cf.  NevertheThe current crisis has increased the need for functional coping with traumatic experiences and negative emotions. While many people experience a downward trend in terms of emotions, depression and trauma, broaden-and-built theory offers insights in how to reverse those processes . As it wThe current review focused on the psychological and behavioral consequences of the lockdown and suggested that the negative effects are serious and may very well outweigh the possible positive effects of the lockdown for the general population . As BrooMS played the primary role in the conceptual conception of the manuscript, and wrote, reviewed, and revised the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


In programs with higher proportions of marks allocated to ongoing assessment, the students have higher overall marks than those with a lower proportion allocated to assessment. Little or no attention has been made to how the allocation affects the academic success of students in individual courses. The purpose of this study was to determine how the allocation of marks to examinations, tutorials and an assignment affects the performance of nursing students in a pharmacology course.For students who passed a pharmacology course (i) the marks for examinations and ongoing assessment  were compared, and (ii) regression line and correlation analysis was undertaken to determine any association between these marks. In addition, for completing students, modelling was undertaken to determine the effects of changing the allocation of marks on passing and failing rates.Nursing students who passed a pharmacology course obtained significantly lower marks in examinations than ongoing assessment, and for the ongoing assessment, lower marks in the assignment than tutorials. Regression line analysis showed that the marks in ongoing assessment  versus examination marks were a poor line-fit. The correlation coefficients between ongoing assessment and examinations were weak to moderate. A high percentage of students passed the course (> 90%) and, modelling for completing students, showed that decreasing the marks for examination would have led to slightly more students passing the pharmacology course with higher grades. In contrast, increasing the marks for examination would have dramatically decreased the number of students passing the course, and their grades.The allocation of marks can have a major effect on student performance. As ongoing assessment is only a weak or moderate indicator of performance in examination this has implications for students who rely on passing examinations for their advancement. For instance, nursing students in some countries (e.g. USA) are required to pass examinations prior to registration, whereas in others  they are not. Consideration needs to be given as to whether it is appropriate for nursing students who fail examinations to pass courses/programs. Historically, examinations, which students had no prior access to, were the most common way to determine academic performance for students. However, over the last 40 years, ongoing assessment (coursework) has been introduced into many degrees , so thatThere are no rules about the proportional allocation of marks for ongoing assessment and examination, and the allocation is often made on a seemingly arbitrary basis and not justified. For instance, in pharmacology courses for nurses in Australia, the proportion of marks for examinations ranges from 40 to 80% –6. The cAssessment can either be summative, which evaluates student learning at the end of the component or course, or formative, which monitors students learning to provide ongoing feedback. Whereas examinations are clearly summative, ongoing assessment can be either summative or formative. One of the reasons for this is that ongoing assessment takes many forms including weekly quizzes, homework, tutorials, laboratory work, oral or poster presentations, and assignments/research projects . Some ofThere is evidence that the marks for ongoing assessment are higher than for examinations, and this has various consequences. Across UK universities, in the programs with higher proportions of ongoing assessment, students had higher overall marks, and consequently better degrees, than those with a lower proportion of ongoing assessment –11. ThisThere have been few studies of the association between marks in ongoing assessments and examination in single programs or courses. Studies have shown that the marks for ongoing assessment were higher than examination marks in a pharmacy program  and in aThe relationship between marked examinations and formative unmarked ongoing assessment has been considered in meta-analysis. This meta-analysis was of the effect of active learning interventions on examination outcomes in the STEM disciplines and showed that the interventions improved examination marks by 6%, and reduced failure rates compared to traditional lecturing . The intThere have been few studies of the relationship between marked formative or summative ongoing assessment and marks in examinations, and these have had varying outcomes. In a pharmacy program, there was only a weak correlation between the marks for ongoing assessment and examinations and no correlation between the marks for a practical write-up and an aligned examination question . In contBeing able to perform well in examinations is especially important for nursing students as it may determine whether they can practice clinically. There are differences in how nursing registration is achieved between countries. In the USA and Canada, it is the marks in national examinations taken after completing their studies, which determines whether the students can practice. In contrast, Australia, the UK, the Republic of Ireland, and New Zealand are among the countries not requiring national examination prior to registration for nursing students but relying on graduation from courses with examinations and ongoing assessment.(i)Do nursing students have higher marks in ongoing assessment than examinations? The hypothesis was that they would.(ii)Do marks in ongoing assessment predict marks in examination? The hypothesis was that they would not.(iii)Does allocating higher proportions of marks to ongoing assessment increase pass rates? The hypothesis was that allocating higher proportions of marks to ongoing assessment was associated with higher marks and pass rates, and vice-versa.(i)To compare the academic performance of students who passed the course in ongoing assessment and examinations.(ii)To determine whether performance in ongoing assessment was a predictor of performance in examinations.(iii)To consider how proportioning marks, between ongoing assessment and examinations, affected the overall marks and pass rates for the passing and failing students.This study was of nursing students in a pharmacology course. The study was repeated in a second semester and second year to determine whether the findings were consistent and ongoing.This is a descriptive study of the relationship between mark allocation to examinations and ongoing assessment  and the academic performance of nursing students in a pharmacology course.The research was undertaken in an Australian university, where students are typically required to achieve an overall mark of 50% to pass a course and passing grades are 4 , 5 (65–74%), 6 (75–84%) and 7 (≥ 85%). Thus, in the pharmacology course, students with < 50% of the overall marks failed the course.In the pharmacology course, 40% of the total marks were allocated to ongoing assessment, which had two components; tutorials and an assignment, both of which were allocated 20% of the marks. The tutorials were both formative and summative and were held weekly in classes of 25 students divided into groups of 5. Prior to the tutorials, the students were given a worksheet, which related to the lecture content for the week before the tutorial. Half of the tutorial marks were given for the completed worksheet, marked at the tutorial. The worksheet preparation was unsupervised and could be undertaken alone or in groups. The other half of the tutorial marks was a group mark for performance at the tutorial, which included questioning by the tutor of individuals and the group about the content of the student preparation.The second 20% of the ongoing assessment was a summative written case-study assignment undertaken outside of class. This case study was an extension of the lecture content in medicines for cardiovascular disease and diabetes. It was a series of questions relating to a case of a person with complex cardiovascular and diabetic issues requiring short/essay answers.The first part of the course was of principles of pharmacology and the second part was of systematic pharmacology. There were two examinations to make up the 60% for examinations. Firstly, there was a mid-semester examination covering the principles  and, secondly, there was a final examination of systematic pharmacology (35% MCQs).The pharmacology course, taken by nursing students in the semesters of 2014 and 2015, had the same content and teacher. In both years, the pharmacology course enrolled ~ 250 students in semester 1 and ~ 360 students in semester 2, and some of these students withdrew or did not complete.P ≤ 0.05 were considered significant for both Student’s t-test and Odds ratios.The author was the coordinator of the pharmacology course, and as such had access to the Microsoft Excel sheets of the marks associated with the course. This data was starting point for the following analysis. In the analysis, For the students, who successfully passed the course, average grades ± SEM were determined. The marks for the individual components ongoing assessment  and examinations were totalled, the total expressed as a percentage, and then the percentages were averaged. The percentages for individuals in examinations and ongoing assessment were compared by Students paired t-test, and the percentages for different cohorts in each component of the assessment were compared by Students unpaired t-test. Mean values were also determined.2 values were also given. In regression, the R2 coefficient of determination is a statistical measure of how well the regression line approximates the real data points, with an R2 of 1 indicating the regression line perfectly fits the data. To determine the strength of the association, the Pearson’s correlation (r) was determined and coefficients of 0–0.19 were considered very weak, 0.2–0.39 weak, 0.4–0.59 moderate, 0.6–0.79 strong, 0.8–1.0 very strong [Regression line analysis was undertaken using Microsoft Excel. Thus, the marks for individual students in examinations were plotted against their marks in ongoing assessment . The equation for the regression line (y = ax + b), where ‘a’ is the slope of the line, and the Ry strong .For all the students who completed the course (i.e. successful and failing students), modelling was undertaken to determine the effect of changing the marking proportions from 40% ongoing assessment/60% examinations had on the pass/failure rates and overall grades. The proportions modelled were changed to (i) 60% for ongoing assessment and 40% for examinations, (ii) 80% assessment /20% examination, (iii) 100% assessment /0% examination, (iv) 20% assessment/80% examination and (v) 0% assessment/100% examination. Mean values ± SEM were determined. Students who achieved less than 50% in the ongoing assessment or examinations were considered to have failed that component for both the actual and modelled data.https://www.medcalc.org/calc/odds_ratio.php.Students who achieved less than 50% in the ongoing assessment or examinations were considered to have failed that component; failure rates for each component were compared by Odds ratio using the online Odd ratio calculator; Ethical approval was obtained for this project from the Human Research Ethics Committee (UHREC) at Queensland University of Technology; Ethics Approval Number 1900000541. The UHREC is constituted and operates in accordance the National Statement on Ethical Conduct in Human Research (2007) and registered by the National Health and Medical Research Council . Under this approval, consent from individual students was waived. Student anonymity was achieved by removing names and students’ IDs from the marks data prior to the study.For completing students, the passing rate was > 90% and the failure rate was < 10% ; Table The modelling changing the allocation of marks, from ongoing assessment to examinations and vice-versa, gave consistent results for all four cohorts of nursing students. Decreasing the allocation of marks to examinations increased the number of students who would have passed the course Table . As the The three major findings of this study of nursing students in a pharmacology course are that for the passing students (i) marks are higher for ongoing assessment than examinations and (ii) there are very weak to moderate relationships between marks obtained in examination and ongoing assessment, and for completing students (iii) increasing the marks allocated to examinations decreased the number of students who passed the course, whereas decreasing the examination marks increased the number of students passing.This is the first study to show that marks for ongoing assessment are higher than for examinations for nursing students in a pharmacology course. Similar findings have been made previously for bioscience courses being undertaken by nursing students  or scienThere are several possible reasons for this disparity between marks in examinations and ongoing assessment. The most obvious of these is that the examination results represent those of the individual student, whereas the ongoing assessment marks may represent that of individuals or groups of students. In the present study, the tutorial mark of 20% is partly a group mark and is composed of 10% for unsupervised preparation/homework, which can be individual or group, and 10% for participation, which is a group mark. This makes it possible that the performance of weak students, and their marks in tutorials, to be artificially enhanced by better students in the group. The assignment component of the ongoing assessment (20%) should represent work undertaken by the individual student, but as this was unsupervised, there was nothing preventing students colluding. One way to overcome this would be to remove group work from courses. However, it is well known that group work is very important skill for nursing students. Thus, we need to be able to overcome this ongoing problem with assessing individuals in group work , 24 or uFor group assignments, self- and peer-rating has been used to overcome varying contributions by students in the humanities  and in pIn the pharmacology course, 55% of the 60% of the marks allocated to examinations were in the form of MCQs. When MCQs are used, the fairest option is to focus on the number of questions attempted and penalize wrong answers, as with this option, blind guessing will on average not help the student . Many unIn this study, we showed that for nursing students in pharmacology, marks in a written assignment were very weak to moderate predictors of performance in examinations. A previous study showed a weak correlation  between marks in a research project and the final examination in a pharmacy course . It woulIn addition, the present study showed that marks in tutorials, which included a homework component, are not good predictors of academic performance in examinations. This is the first time that this has been shown for nursing students or in a pharmacology course. However, this finding is not consistent for all disciplines, as marked tutorials have been shown to improve marks for courses in calculus, macroeconomics , financeIncreasing the marks allocated to examinations increased the number of students who failed the course and decreased the number who passed. With the allocation of marks of 60% to examinations and 40% to ongoing assessment, in the present study, the number of students who failed the pharmacology course was low (5–8%). With this low failure rate, the likelihood of increasing the passing rate by changing the allocation of marks was low, and our modelling confirmed this by showing that the passing rate could only be increased by 2–6 percentage points by increasing the marks allocated to ongoing assessment. With this allocation, the passing rate was high, 92–95%, and this occurred despite 20–26% of students failing the examination component of the course.The major finding of the modelling part of our study was to show that increasing the marks allocated to examinations would have decreased the number of students who passed the course in pharmacology, with 19–25% failing overall if all the marks had been allocated to the examination. In Australia, the allocation of marks for examination in pharmacology or pharmacology-related courses from nursing programs ranges is variable . Thus, Although our modelling was done for a pharmacology course, the findings will apply to any course where the students have weaker outcomes in examinations than ongoing assessment, which is common –13. As, As marks are higher for ongoing assessment than examinations, the concern is that the nursing students, who pass the ongoing assessment by obtaining 50% of the allocated marks, but not the examinations, may not have assimilated the necessary knowledge in pharmacology or other courses, to continue their program of study. Thus, the disparity between marks in examinations and ongoing assessment needs to be considered, and methods introduced to overcome this. One possible practical solution to this dilemma of whether students who pass ongoing assessment but fail examination, should be allowed to pass courses and progress in their studies, would be to make it compulsory for the students to pass the examination component of the course.These findings have implications for those countries  where performance in undergraduate ongoing assessment is partly used to determine whether nursing students/graduates go on to clinical practice. In Australia, assessment for nursing students is commonly a mixture of ongoing assessment and examinations to give a Grade Point Average (GPA), and for many nursing courses/programs, most marks are from ongoing assessment. Thus, in the present nursing program at the university where the present study was undertaken there are 23 compulsory and one elective course. Seven of the courses are off-campus (practicums) and are marked as satisfactory or not satisfactory. Of the remaining 16 compulsory courses, 8 have no examinations, and 78% of marks are allocated to ongoing assessment and only 22% to examinations. It seems likely that the number of students who failed the examination components in our Australian university but passed the program overall, would have failed the NCLEX-RN examinations in USA system and not have been registered. Further consideration needs to be given as to whether students in Australia, who do not undertake or fail examinations, are fit to practice.One possible practical solution to this dilemma of whether students who pass ongoing assessment but fail examination, should be allowed to pass courses and progress in their studies, would be to make it compulsory for the students to pass the examination component of the course. In addition, studies need to be undertaken that consider the relationship between success in undergraduate courses and clinical practice. Another practical solution is to adopt the system used in the USA, where after completion of an undergraduate course in nursing, success in a national examination, NCLEX-RN, is a requirement for clinical practice.A limitation of this study is that it only used basic statistical analysis with Microsoft Excel, and more complex analysis could have been undertaken with other statistical packages . However, the major limitation of this study is that it is of a single course in pharmacology, and that some of the findings may not relate to other courses being undertaken by nursing or non-nursing students. However, we have previously shown a similar reliance of marks in ongoing assessment for the overall success of nursing students in a bioscience course . Also, tMore attention needs to be given to the allocation of marks between ongoing assessment and examinations. Marks in ongoing assessment may be a poor indicator of success in examinations. Students can fail the examination component by obtaining 50% or less in the examination, but pass the course, and increasing the marks allocated to ongoing assessment accentuates this finding. Students, who pass the course but not the examinations, may not have assimilated the necessary knowledge to continue in their program. Additionally, some of the passing students may have passed overall due to work done by others in ongoing assessment. It is suggested that it should be compulsory for the students to pass the examination component of the course.


Most urban areas of the Po basin in the North of Italy are persistently affected by poor air quality and difficulty in disposing of airborne pollutants. In this context, the municipality of Milan started a multi-year progressive policy based on an extended limited traffic zone (Area B). Starting on 25 February 2019, the first phase partially restricted the circulation of some classes of highly polluting vehicles on the territory, in particular, Euro 0 petrol vehicles and Euro 0 to 3 diesel vehicles, excluding public transport. This is the early-stage of a long term policy that will restrict access to an increasing number of vehicles. The goal of this paper is to evaluate the early-stage impact of this policy on two specific vehicle-generated pollutants: total nitrogen oxides (NO Air quality monitoring is one of the major challenges that European institutions jointly with national and local administrations are facing in terms of environmental protection. In particular, the 2008 European Air Quality Directive (AQD) 2008/50/EC  requiresIn areas such as Northern Italy, where the industrial transition in the 1990s reduced coal burning and sulphur concentration, the large majority of environmental studies focus their attention on toxic pollutants that are produced by thermic vehicle engines and house heating plants. These are known to generate serious health effects . Total nAccording to the above EU rules, governments adopted standards and quantitative limits for pollutant emissions to make economic agents responsible and implement abatement policies. In particular, the maximum concentration for NO finance  and tour finance .https://www.comune.milano.it/aree-tematiche/mobilita/area-b), where the access and circulation for the most polluting vehicles, as well as those longer than 12 meters, have some partial restrictions, enforced by a monitoring system of entrance gates controlling each license plate and imposing a fine on unauthorized vehicles. The access prohibition concerns Euro 0 petrol vehicles and a large part of Euro 0, 1, 2, and 3 diesel vehicles, with specific exemptions for public transport, itinerant traders, and residents, and it is active from Monday to Friday during business hours (from 7:30 a.m. to 7:30 p.m.), except holidays. According to the municipality of Milan, the share of cars registered in the Milan metropolitan area and involved in the restrictions in 2019 is close to 17%, while the share of freight transport vehicles is around 53% [The present paper analyses the introduction of the first phase of an air quality control policy in the municipality of Milan, which started on 25 February 2019 and directly acts on traffic rules. The administration defined an extended limited traffic zone, named Area B . The agency manages a diffuse monitoring system distributed among the regional territory and counting on hundreds of monitoring stations collecting intra-daily information on climate and pollution through sensors.Data on pollution and weather conditions of Lombardy are collected from the Lombardy Regional Agency for Environmental Protection (ARPA Lombardia), which makes a large open data portal fully available to users , Marche (ARPA code 501), Senato (ARPA code 548), and Verziere (ARPA code 528) are urban traffic control units: sensors installed near important roads and intersections in order to accurately quantify the pollution generated by traffic. The station Città Studi (ARPA code 705) is instead of type urban background, that is, the station is located in such a position that the level of pollution is not mainly influenced by specific sources but by the integrated contribution of all the upwind sources at the station with respect to the predominant directions of the winds on the site [The spatial distribution of the stations is not uniform: air quality stations cover northern, eastern, central, and southern parts of the city, leaving the western districts uncovered; climate stations cover in detail the city centre and all the northern neighbours but are not installed in the south.The analysis presented in this paper takes into account daily measures from 1 January 2014 to 30 September 2019, generating an overall sample of 2099 daily observations.The whole, the monitoring system provides information about many urban pollutants, such as carbon dioxide, particulates, and oxides. All the pollutants are measured as Weather stations provide measures of local temperature , Brescia (far East), Cremona (far Southeast), Lodi (Southeast), Pavia (South), Saronno (North) and Treviglio (East). As reported in Before investigating the factors and causes that may have generated these sharp reductions, we perform a preliminary analysis of the concentration levels pre-and-post policy, in order to quantify the changes observed in 2019 both in Milan and in the other centres. Since air quality data present outliers and heavy-tail distributions given by extreme events, the only use of average values for central tendency estimation can provide misleading results. Therefore, we compare the central values obtained both considering the sample mean and the sample median, which is notoriously a more robust indicator if outliers occur ,27.The comparison is performed through the computation of two statistics based on the difference of central tendency indicators. The first statistic computes the difference between the average of the observations gathered after the policy intervention and the average of observations referring to the sub-period 2014–2018. The second statistics consists of computing the difference between median concentration levels observed in 2019 and before that year. The difference in average concentrations is denoted by j, where i, where j is computed as Both statistics use the observations collected between 25 February and 30 September of each year, with a total length of 214 days. Approaches of this type can be framed in a context of treatment-control analysis, in which the data referred to the year 2019 constitute the treatment group, while the observations collected between 2014 and 2018 compose the control group. Control data refer to a 5-year-period; therefore, the concentrations measured on the same calendar day are aggregated into a single representative value calculated as the daily average concentration of the period 2014 to 2018. Denoting as Let In this section, we discuss the time series models used to identify the policy effect, the estimation algorithms, and the related inference. Firstly, we introduce a brief description of the basic structural model (BSM) using a state space approach for time series analysis and the estimation algorithm based on the Kalman filter ,29. ThenAccording to their physical characteristics, air pollution concentrations time series are often characterized by seasonality, high persistence ,31, stroBSM is defined as a simple unobservable components model composed by local linear trend (LLT), stochastic seasonality, and irregular (white noise) component. LLT describes both the temporal evolution of the series level and its slope, while the seasonal component aims to capture cyclical behaviors given by natural and anthropogenic phenomena. We modeled the seasonal component using a trigonometric form for daily data, hence with period Let Equation  is calleWhen dealing with Gaussian linear state space models, the parameters estimated using a maximum likelihood (ML) approach inherit the asymptotic properties of ML estimators . The disWe now propose a three-step procedure for model selection, which considers multiple rules based on cross-validation, information criteria, and stepwise regression. To avoid estimation bias due to the policy introduction, all the steps are computed using only the observations before the introduction of Area B that is, from 1 January 2014 to 24 February 2019.k for the trigonometric function. Specifically, we fit 10 alternative models for each station: in each of them, the trigonometric seasonality is modeled by an increasing number of harmonics ranging from Step 1 is designed for selecting the most predictive seasonal component, defined in Equation , compariOnce the seasonal component has been selected, step 2 introduces in Equation  a countequations , and γt In step 3, we identify the best subset of calendar events and weather covariates, capturing residual variations not yet covered by the counter-factual or by the latent components. These residual variations are estimated by the smoothed observation disturbances from Equation  that is Relevant weather and calendar covariates are selected through a backward-forward stepwise regression algorithm, which uses as a starting model the auxiliary linear regression expressed in Equation . The equThe stepwise regression is set up twice for each station: in one case, it selects the model according to the Akaike’s Information Criterion (AIC), while, in the other, it uses the Bayesian Information Criterion (BIC). The algorithm starts estimating the full model and computes the AIC or the BIC. Iteratively, it drops out the predictors one at a time; at each step, it computes the new information criterion and considers whether the criterion is improved by adding back in a variable removed at a previous step. The procedure ends when the reintroduction of each omitted variable does not improve the information criteria.In the first two steps, we select the seasonal component and the counter-factual term by fitting and comparing alternative models based on Equations from  to 4) a a4) accoAccording to the cross-validation principle for time series ,36, we sThe introduction of new rules or limitations to individual behaviors can lead to the co-existence of multiple effects with different structure, such as simultaneous immediate changes and adaptive changes that take a long time before visible effects occur. Take into consideration that this fact leads to implement intervention analysis, which includes both permanent and transitory effects. Further details and examples of ARMA-like transfer function applied to intervention analysis are available in Pelagatti .The policy intervention is modeled through the combination of two individual effects: (1) a permanent effect, estimated by quations , γt is tKFAS package [ggplot2 [sf [All the statistical computations and figures have been carried out using the statistical software R . For sta package  was used[ggplot2  and sf [lot2 [sf .In this section, we present and comment on the empirical results relating both to the differences between pre-and-post policy averages and medians and to the policy intervention analysis for the Milan Area B case study. Empirical differences of concentrations levels for all the considered stations are presented in The estimates highlight large negative differences in oxides concentrations between 2019 and the period 2014–2018, both in the metropolitan area of Milan and in almost all the surrounding towns. Particularly heavy reductions, and similar to those in Milan, were recorded in the cities of Bergamo (East) and Pavia (South). The simultaneous abatement inside and outside Milan confirms the presence of a general decreasing trend in the aggregate levels of pollutants for the Lombardy Po basin as already indicated by the previous figures.The differences registered in Milan are relevant both in suburban districts, such as the stations Liguria (West) and Marche (North), and in the historical centre at the Senato station. For those monitoring stations, the reductions are larger than 16 These preliminary results do not allow for identifying the causes of the reductions and to state if they depend on common causes related to the environment and climatic factors or if they have been generated by the introduction of the new policy in Milan. The next section will attempt to investigate the variations through the modeling of possible environmental and anthropogenic factors able to influence the air quality of the city.Results relative to the first step of model selection are summarized in After selecting the seasonal component, we proceed to the selection of the counter-factual term. Estimates are summarized in The search for the optimal counter-factual term requires greater attention and detail than in the previous step as the minimizers are not unique. According to the plots, there is a restricted set of stations that are good candidates for the counter-factual role. The set includes the following cities: Treviglio, Pavia, Saronno, and Cremona. In particular, Pavia’s station achieves one of the best forecast and fitting performances for almost all the stations in Area B for both NOThe last step of model selection aims to select the optimal subset of local weather and calendar regressors after having selected the optimal unobservable components, common weather, and socio-economic factors, captured by the counter-factual. For each station and pollutant, the best models are reported in As expected, BIC-based models, being more parsimonious, retain fewer covariates than AIC-based models. Following this fact, we will use the BIC-selected models, but we now discuss some details about the selection process. Concerning the calendar, both criteria include in almost all cases the holidays, week-end, and Sunday holidays effects. AIC suggests adding also the interaction term between Sunday and holidays. Even if the interaction between Saturday and holidays is not always included, the final model will take into account the full set of calendar events and their interactions.Regarding weather covariates, except for the wind speed, none of the others is included within the final models. Winds blowing from Southwest (QBased on the results of the three-step model selection procedure, the final specification of the BSM augmented by the policy intervention can be expressed using the following model:In this section, we show the numerical results obtained using state space modeling to estimate both permanent and transitory effects generated by the introduction of Area B controlling for local weather conditions, anthropogenic effects, and common areal trends.The maximum likelihood estimates of the coefficients and the components’ variances for the five air quality monitoring stations installed in Milan are reported in The short-term impacts adjusted for common anthropic and weather factors are summarized in Such a result could be justified by the presence of multiple causes. As a first justification, we are approaching the initial phase of a progressive policy and the time elapsed since its outset may be too short to assess any significant impacts on pollutant levels. This fact is consistent with the forecasts expected by the municipality of Milan about the reductions in nitrogen oxide levels; in fact, the first significant reductions should be observed starting from 2022 . FurtherThis paper analyzed the early-stage effects on air quality of the new traffic policy in Milan, the so-called Area B. The concentrations of nitrogen oxides (NOThe first hypothesis in the introduction inquires about the presence of a significant effect on the air quality of the city. As a first point, the preliminary results show that concentrations during spring and summer 2019 are lower than during the same seasons in the previous five years, hinting for a reduction effect due to the policy. On the other side, a similar reducing trend has been observed in various neighbouring cities around Milan, which belong to a homogeneous meteorological, social, and economical cluster within the Po valley. Their similar behavior is used here as an areal common trend capturing both weather and anthropogenic components. Our approach, which adjusts for local weather conditions and the areal common trend, does not provide a further reduction effect for any station comparing to this trend. Instead, in Senato station, which is inside the historical city centre and was already covered by Area C, the estimates provide a strong, but moderately statistically significant, increase for both considered pollutants. This is coherent with the fact that the restriction introduced is very limited as it concerns just some classes of old vehicles, which are a small percentage of the entire vehicle pool, both in terms of number of cars and emissions.Since the first research hypothesis is confirmed just to a minor extent and with an opposite sign with respect to what was expected, the second research hypothesis, concerning the homogeneity of the possible effects, assumes now only a technical scope. It is confirmed just for what concerns the positive direction of the changes, but not for their significance. In fact, among all the estimated permanent effects, only Senato station is significant at 5%. Moreover, the estimated transitory effects are always not significant at any confidence level.The above facts hint that, compared to the common trend of the considered area, Milan air quality is improving slowly, and, in this sense, the first phase of Area B seems to have a negative effect on air quality. Due to the limited scope of this first phase and its progressiveness, it is not unexpected to find a limited or a zero effect. Nonetheless, the negative effect needs some more explanations.Although finding the ultimate motivation for this is not the aim of this paper, a discussion follows. Firstly, the statistically significant increase found is limited in space and is located inside the previously introduced restricted Area C. It may be possible that this further restriction increased congestion of public transport buses, which are often very old vehicles, or to the aforementioned adaptation shocks. This could explain only a part of the results. In fact, this first point is also related to the other sources of nitrogen oxides. According to INEMAR , road trIn conclusion, although environmental protection policies are in general a fundamental step for sustainability improvement, in some cases, they may not be sufficient or their implementation may be misleading. In our case, we considered only the early-stage of a policy, which is progressive in time. Hence, the results of this paper may be regarded as physiological, provided that they characterize only the initial part of the policy implementation and are improved soon. It follows a recommendation to the municipal government to develop the policy more strongly.Additional research could be developed in the future. In particular, the effect on traffic congestion inside Area C could be investigated further using historical data related to the vehicle movements crossing the access points. Moreover, the use of a multivariate approach, which includes other pollutants such as PM