Predation on swarms of prey, especially using visual information, has drawn much interest in studies of collective movement. Surprisingly, in the field of biosonar this aspect of prey detection, which is probably very common, has received little to no attention. Here, we combine computer simulations and actual echo measurements to accurately estimate the echo sound pressure of insect swarms of different size and density. We show that swarm echo sound pressure increases with 3dB for every doubling of insect number, irrespective of swarm density. Thus swarms will be much easier to detect than single insects. Many of the insects bats eat are so small that they are only detectable by echolocation at very short distances. By focusing on detection of swarms of insects, a bat may increase its operating range and diversify its diet. Interestingly, interference between the sound waves reflected from a swarm of insects can sometimes result in echoes that are much weaker than echoes from single insects. We show that bats can reduce this problem by increasing the bandwidth of their echolocation calls. Specifically, a bandwidth of 3–8 kHz would guarantee receiving loud echoes from any angle relative to the swarm. Indeed, many bat species, and specifically bats hunting in open spaces, where swarms are abundant, use echolocation signals with a bandwidth of several kHz. Our results might also explain how the first echolocating bats that probably had limited echolocation abilities, could detect insects through swarm hunting. When bats hunt, they often encounter insects that fly in swarms. Echolocating bats emit sonar signals to search for prey and it is currently unknown what such swarms look like to a bat. Unlike vision, sonar senses the delay or distance to objects directly. We show that when bats hunt for insects in the sky, the echoes from the insects in a swarm will most of the time sum up and therefore become much louder than the echo of a single insect. Every time an insect swarm would double in number, a bat would hear an echo that is 3dB stronger. This could enable a bat to detect prey from longer distances and some bats might thus profit from swarm hunting. However, the echoes reflected from the many insects in the swarm also create acoustic interference so that sometimes the summed echo is actually weak at a certain frequency. We show how bats could deal with this drawback. It is known that most bats do not use sonar signals with a single tone but that they modulate their tones. Our analysis shows that this modulation can solve the problem of spectral interference ensuring that the swarm-echo is always loud. Hunting on animal aggregations has been studied from many perspectives including the potential benefits for the prey  and theOnychonycteris finneyi) were able to fly ). Chironomidae, Blissidae, Noctuidae, Hydropsychidae, Formicidae), due to active gathering or wind. A swarm of 200 small insects compact enough to be covered by the echolocation beam would lead to a gain of 30dB (from -73dB = -43dB), has an echo intensity equivalent to that of a large moth (wing-surface 200mm2) that can be easily detected by a bat [The advent of DNA barcode analysis has made it possible to determine which species of insects are eaten by bats .Our results show that the randomness of the spectrum due to swarming-targets depends little on the shape of each of the elements (point targets vs mosquitoes in various orientations). The noise introduced by the constellation most likely overrides the randomness in the echoes of each of the individual targets.Hunting for swarms could thus increase both the chances of finding food and the variety of consumed food items, enabling bats to detect and feed on small insects. Swarm hunting could thus explain how echolocation has evolved to enable insect hunting. It is believed that the earliest bats were insectivores, but the detection of small single insects using bio-sonar is extremely difficult today, and was probably much more difficult with rudimentary echolocation and rudimentary brain processing of echolocation. One possibility is that early bats only ate very large insects, but that would strongly limit their diet. We show that the tendency of insects to aggregate in swarms facilitates the detection of small insects, which would have substantially broadened the diet of early bats. Swarm hunting could thus have mediated the evolutionary transition to more sophisticated echolocation systems. Detecting smaller and smaller swarms of insects down to detecting single insects of small size could have made this transition infinitely smooth.As flocking, swarm formation- and movement have become a popular topic in biology (e.g. ), a numbIn this paper, we will refer to a swarm realization as a newly generated swarm consisting of the same number of targets, but in totally new positions while still having the same statistical properties (i.e. same N and R). When taking into account the inter-insect distance (R) and the number of insects, our simulated swarms had overall diameters of 0.13; 0.31; 0.52 and 1m for our swarms of R = 15; 35; 60 and 120mm respectively (for N = 300). We generated swarms with the following sizes: 1, 2, 4, 8, 16, 32, 50, 70, 90, 110, 130, 150, 200 and 300 targets. The diameter of any of our swarms declined with decreasing the number of insects similar to what was reported for Chironomid-clouds (Kelley and Ouellette 2013[The echo sound pressure (at any frequency f) from any realization of a random point cloud, consisting of N targets was calculated as:i is the spherical loss factor for each target i, λ is the wavelength and -j denotes the imaginary part of a complex number (j^2 = -1). Square brackets denote rounding to floor (obtaining the integer). The factor ai denotes the 6dB loss in pressure with doubling of distance relative to 0.1m from the target (where target strength was set to -60dB) on a linear pressure scale. This equation does not specify atmospheric attenuation. In calculations where atmospheric attenuation becomes relevant  from every angle with steps of 5 degrees. Next, we used this dataset as a lookup table to generate the echo of an insect from any desired orientation, or in our case; to design a swarm of insects each in any desired orientation. The insects were positioned in space just like the point reflectors (see above) but we also had to determine their orientation. In real insect swarms, individuals tend to align the orientation of their bodies. To mimic this, all insects were horizontal relative to earth facing the same direction. Since in reality insects are not always perfectly aligned, we mimicked a swarm in which yaw- and roll- angle varied probabilistically with +/- 30 degrees. The bat was facing the center of the swarm (2m from the center) viewing it from above with an angle almost perpendicular to the wings  so that the wings produce loud echoes. To verify the robustness of the data generated with this type of swarm, we also carried out control-simulations, one in which the swarm was ‘seen’ laterally (same +/- degree Gaussian spread) and one in which the alignment of the insects within the swarm was completely randomized.Finally, in order to estimate the insect-swarm echo, we used To estimate the angle dependent insect frequency response, we used a 3D mesh model of a mosquito. The model was complete with eyes, antennae, legs, wings and body segmentation. Using Meshlab , we checWe calculated the echo spectrum for each target constellation between 12–80 kHz. From each spectrum we calculated the peak and average pressure (in dB), and took the average over 100 swarm realizations per condition  and plotted the result against the number of point targets.For three frequencies  we increased bandwidth with steps of 500 Hz down to 12.5 kHz below these upper frequencies, and calculated the maximum echo pressure for each echo (note that these maxima could occur at any frequency). For each bandwidth, we generated 100 simulations (each with a different stochastic swarm arrangement) from which we selected the 25 simulations that had the lowest maximum pressure values. We then plotted the mean of the maximum pressure value for these 25 simulations against bandwidth indicating that the bat could ensure receiving an echo with this sound pressure (or more) in 75% of the time. We also tested a more conservative value of 90% probability, following the same procedure and plotted the result in In order to evaluate the different simulation tools used in this paper, we also created a point cloud in real life, using wooden spherical beads. We also simulated this exact same cloud (of different sizes N) mathematically, using the same methods that were used to generate the simulations of insect swarms in this paper. We then compared the results of our simulations with each other and with recordings of real echoes of the real beads. To measure the echoes of real sphere constellations (n = 1…16), we suspended wooden beads (2cm diameter) on 0.1mm diameter cloth strings in an acoustic chamber. Prior to ensonification, 2 IR reflectors  were placed on opposite sides of each bead. A system of 20 tracking cameras  was used to measure the exact locations  of each bead. The accuracy of this camera system was proven to have accuracy of ~1mm in an extensive series of performance- and verification tests. The louA series of 15 narrowband sound pulses (3.5ms each) together covering the spectrum of 12 to 80 kHz were emitted at the spheres using the Avisoft Vifa loudspeaker (connected to an Avisoft 116 Player D/A). The signals were 15 linear frequency modulated sweeps with a bandwidth increasing from 4 to 14 kHz around a center frequency increasing exponentially from 12.2 to 76.2 kHz from sweep 1 to sweep 15. This signal design kept the wavelength change of the emitted pulse below a factor 1.8 for nearly all signals. Keeping the wavelength-change constant ensures that the potential echo-information is spread equally across channels. The echoes were recorded using an Avisoft CM16 condensator microphone , attached as close as possible to the loudspeaker. This was repeated 9 times. Then the microphone was placed in the middle of the sphere-cloud, the spheres were removed and the same sound was recorded 9 times in order to estimate the emission’s frequency response and compensate for it (see below). Both paradigms were done with neutral phase and opposite phase . For every condition mentioned above the 9 spectra for each frequency band were averaged. The 15 averaged spectra were then merged from 12 to 80 kHz. This was done for the microphone in both positions. The final spectra from both positions were normalized and the ratio calculated between both spectra. This ratio is the difference between the incoming sound to the spheres and the sound reflected from the spheres. It is therefore the same as the echo of the spheres if a flat spectrum were used .Culex pipiens) were recorded at the brackish water inlets of Goeree Overflakkee, Netherlands, GPS 51.8038N 3.9649E. We used the same equipment as described under “Echo measurements”. The emitted pulses were 1ms sweeping from 55 to 23 kHz. We compared the echoes of the swarm with echoes from a single tethered insect (0.1mm wire diameter) suspended in an anechoic room at 50cm distance from the loudspeaker and microphone. We verified from the time between emission and echo in the recording that in the real insect swarm, mosquitoes were present in front of the loudspeaker at the distance used to ensonify the single insect in the lab, thus enabling us to compare echo strengths , we calculated the number of pulses that would be required to emit in order to match the detection performance at maximum bandwidth (12.5 kHz). We did so by examining the statistical echo-sound pressure distribution for each signal bandwidth (we simulated 100 swarms per bandwidth). We then measured how often the echo sound pressure (for a given bandwidth) was above the mean sound pressure for a full bandwidth (12.5 kHz bandwidth) signal. The calculation of this probability enabled us to estimate how many pulses are needed at each bandwidth to maintain equivalence with the detection performance of a 12.5 kHz pulse.S1 FigUpper graph: Solid line R = 15mm, dashed line R = 200mm, colors denote different frequencies. Lower graph: R = 35mm, but with different swarm sizes. Results from this simulation reveal similar principles as in (TIF)Click here for additional data file.S2 FigUpper graph: Solid line R = 15mm, dashed line R = 200mm, colors denote different frequencies. Lower graph: R = 35mm, but with different swarm sizes. Results from this simulation reveal similar principles as in (TIF)Click here for additional data file.S3 FigFor each spectrum, the bandwidth of all troughs between pairs of peaks was measured. Peaks were defined as peaks that are no more than 20% lower than the maximum of the entire spectrum . The bandwidth of a trough can be thought of as the minimal bandwidth allowing to receive a peak pressure for a given swarm realization. Red line shows that a bandwidth of 10 kHz would ensure that the bat receives the maximal echo  for 70% of the echoes (the line parts the histogram to 30:70%). We repeated this analysis for different peak criteria  and the pattern is the same (the bandwidth would obviously change accordingly). We call this point of 70% BWc–the critical bandwidth.(TIF)Click here for additional data file.S4 FigBWc was extracted as in (TIF)Click here for additional data file.S5 FigWorst cases: in the minimal bandwidth case (500 Hz), a bat is faced with the worst spectral trough of 100 simulations. The figures show that adding bandwidth improves swarm detection by 15-20dB on average in this worst case scenario. (A) Echo sound pressure as a function of bandwidth for a 100-reflector swarm with three different upper -frequencies (colors) and two different inter-reflector distances (15 and 120mm represented by solid vs. dashed lines respectively). (B) The same as in (A) but for 100-mosquito-like reflectors. (C) The echo sound pressure as a function of bandwidth for a point-reflector swarm with different numbers of reflectors (depicted by different colors). The upper frequency was 25 kHz for these simulations. (D) The same as in (C) but for mosquito-like reflectors. Each point  is based on generating 100 stochastic swarm realizations, calculating the loudest peak over the relevant bandwidth (depicted on the x-axis). Note that the lowest bandwidth is not 0 Hz, but 500 Hz, therefore displaying only intended bandwidth and not natural variation see .(TIF)Click here for additional data file.S6 Figi, calculating the power spectrum of their echoes (using i for a single swarm with 10-points (red) and the impulse response generated according to We tested the validity of the analytic model  numericaes using , and the(TIF)Click here for additional data file.S7 FigTop: the spectrum of a 7-reflector model is presented. Bottom: the spectrum of an 8-reflector model is presented. For a better comparison, the spectrum between 30–42 kHz is enlarged. Note that the real beads had a 2cm diameter which could explain the slight differences between the actual echoes and the simulated ones.The spectrum of three models is presented: the analytic model (red), the boundary element model (blue) and the actual recording of real beads (in black and in magenta for echoes for which the emission was shifted by 180 degrees). (TIF)Click here for additional data file.S8 FigThe spectrum of two models is presented for a 10-reflector model: the analytic model (red), the boundary element model (blue). We do not show the data for the physical bead model because it would be difficult to see with another graph overlaid.(TIF)Click here for additional data file.S9 FigA bat can either increase its bandwidth ((TIF)Click here for additional data file.


Janus kinase 2 (JAK2) inhibitors represent a promising therapeutic class of anticancer agents against many myeloproliferative disorders. Bioactivity data on pIC Cancer exerts a great impact on the quality of life and is a leading cause of death worldwide. Although cancer chemotherapy, one of the major medical advances in the last few decades, is directed toward certain macromolecules to treat cancer, it cannot efficiently discriminate between normally dividing cell and tumor cells, leading to unwanted toxic side effects. However, targets are usually located in tumor cells, thus providing a high specificity toward tumor cells and broader therapeutic window with less toxicity is beneficial. Therefore, targeted therapy represents a promising approach to cancer therapy . GeneralJanus kinase 2 (JAK2) is a member of the Janus family of tyrosine kinase, which plays an important role in many cellular signaling pathways ,4. It isJAK inhibitors are important class of targeted therapy that interfere with specific cell signaling pathways, which allows target-specific therapy for selected malignancies. Many of the JAK inhibitors are known to interfere with the JAK-STAT pathways, which have an implication in the treatment of different types of cancers and inflammatory diseases ,9,10,11.Quantitative structure activity relationship (QSAR) is an approach for elucidating the origin of biological activity with their respective chemical compounds represented as descriptors. The QSAR models can reveal molecular features that are essential for active compounds and that can subsequently be used as therapeutic agents . SeveralHere we propose a large-scale QSAR investigation for predicting JAK2 inhibitors. Several statistical methods were used to build regression models in which inhibitors were represented as highly interpretable substructure fingerprint descriptors to understand the underlying JAK2 inhibitory activity, which is performed according to the guidelines of Organisation for Economic Cooperation and Development (OECD) . This maIn order to provide the chemical space of JAK2 inhibitors, Lipinski’s rule-of-five descriptors are analyzed. This may provide insights on the origin of inhibitory properties of compounds. Lipinski’s rule-of-five descriptor consisted of molecular weight (MW), octanol-water partition coefficient (ALogP), number of hydrogen bond donors (nHBDon) and number of hydrogen bond acceptors (nHBAcc). Scatter plot of ALogP vs MW of the JAK2 inhibitors coloured by activities is shown in Usage of substructure fingerprint descriptors allows us to pinpoint the substructures that are important for modulating activity of JAK2. In order to get rid of the redundancy among the descriptors, the substructures were filtered using a cutoff threshold set at 0.70. As previously mentioned, the initial data set was split into a training set and test set, where the former represented 80% of the data set while the latter constituted the remaining 20% of the data set. To avoid the random seed, data splitting was performed for 100 iterations where each split was used to create a predictive model. The mean and standard deviation of the resulting predictive performance (QSAR models were developed with various machine learning methods consisting of rule based models (DT), ensemble models (RF), non-linear models (SVM) and deep learning (DNN). As shown in the  of 0.97 , 0.97 [2 of 0.97 , 0.929 [ of 0.97 , 0.970 [ of 0.97  and 0.86 of 0.97 , respectScatter plots of R models .The models built on JAK2 has an excellent performance for RF as judged from the cross-validation set and test set. The performance of the cross-validation set is 2 > 0.5) . The maredictive . Figure The analysis of feature importance for different types of substructure fingerprints provides a better understanding of the JAK2 inhibitors. The AD of the QSAR was defined as to assess the credibility of the model via the Williams plot, shown in To identify privileged scaffolds, cheminformatics approach was utilized to deduce privileged scaffolds giving rise to high inhibitory activities against JAK2. Privileged substructures are a concept introduced in which they are capable of making compounds that display potency for more than one receptor, providing viable alternatives when searching for new receptor inhibitors .Scaffolds analysis is performed with the following steps: (1) compounds are clustered within the Tanimoto Similarity of 0.80 (2) clusters N > 19 are retained for further analysis (3) mean pICnst JAK2 .A data set of inhibitors against the human JAK2 were compiled from the ChEMBL 22 database, which is comprised of a total number of 6772 bioactivity data points from 3906 compounds . CompounUnderstanding biological, chemical and physical properties of chemical compounds is a central issue in pharmaceutical bioinformatics. With what accuracy this bioactivity can be predicted solely depends on how chemical compounds are described. Several molecular descriptors have been introduced with the aim of finding the most suited descriptors to relate these properties ,56,57,58cor function from the R package stats was used to calculate correlations among descriptors. To obtain filtered descriptors with all pairwise correlations less than 0.7, the findCorrelation function from the R package caret with a cutoff at 70% was used [Collinearity is a condition where a pair of descriptors have a substantial correlation with each other. In general, it is desirable to avoid data with highly correlated predictors. Not only do redundant predictors frequently add more complexity to the model than the information they provide to the model, which adds computational cost and time, but they also over-fit the model ,62. Overwas used . The remsample function from the R base package was used to split the data [To avoid the bias that may arise from a single split when creating training models , predictthe data . BrieflySupervised learning enables the model to make prediction about unseen or future data by learning from labeled training data. Regression models were constructed for the prediction of the continuous response variables as a function of predictors.rpart function from the R package rpart was used to build the QSAR models [Decision Tree (DT) is a rule-based algorithm in which construction involves top steps, which are growing and pruning. Growing starts from root node which are branches out to form internal nodes. Internal nodes represent descriptors, branches describe descriptors value sand leaf nodes represent dependent variables  is a machine learning that can be used to perform both classification and regression in which kernel function is used to map the data into high dimensional feature space. Commonly used guassian radial basis was used to build the model. The R models .keras was used to build QSAR models [Deep neural network (DNN) is a method that imitate human brain comprising networks of interconnected neurons that function in relaying message in the form of electrochemical signals. DNN maps inputs to a target through a deep sequence of simple data transformations. The sequential model from R package R models .randomForest function from the R package randomForest is used [Random forest (RF) is an ensemble model that is comprised of multiple decision trees. Optimal tuning parameters  for RF were obtained by training the model with different ranges accompanied with 10-fold cross-validation. The  is used .Model validation is an essential process for assessing the performance of the predictive model. The following statistical metrics were used to evaluate the performance of the QSAR models: coefficient of determination (R2) , root meR2) , rm2 [74m2 [Δrm2 . The R2 QRQThe 10-fold cross-validation technique is one of the most frequent statistical evaluation in which 10 percent of data is left out as a test set while the remaining data is used to build model (Q2) . This prrelation . The orihhp is the number of substructure fingerprint count and n is the number of samples in the training set.The applicability domain is an essential concept in QSAR which can be used to estimate the uncertainty in prediction of a particular molecule based on the distance to the compounds used to build the model . Leveragcmp.cluster function from the R package ChemmineR was employed to cluster the chemical structures [Binning clustering was utilized to cluster compounds in which the Tanimoto similarity cutoff is set at 0.8 to ensure that similar chemotypes are clustered in each group. The ructures . Singletructures .Computational approaches for predicting the activities of JAK2 inhibitors can facilitate drug discovery efforts by saving cost and time. QSAR modeling was performed using the substructure fingerprint descriptors as an input to determine the importance on the inhibitory properties of the JAK2, which provided excellent predictive performance for both cross-validation and the test set. By utilizing the Gini Index of RF, heteroaromatic substituents  are shown to have significant weight in improving the potency of the JAK2 inhibitors. Molecular cluster analysis revealed that pyrazine scaffolds have nanomolar potency against JAK2. Such insights can provide a better understanding of the origin of the JAK2 inhibitory properties and may be used as a reference for designing novel modulators.


India comprises much of the persisting global childhood measles mortality. India implemented a mass second-dose measles immunization campaign in 2010. We used interrupted time series and multilevel regression to quantify the campaign’s impact on measles mortality using the nationally representative Million Death Study . 1–59 month measles mortality rates fell more in the campaign states following launch (27%) versus non-campaign states (11%). Declines were steeper in girls than boys and were specific to measles deaths. Measles mortality risk was lower for children living in a campaign district  or born in 2009 or later . The campaign averted up to 41,000–56,000 deaths during 2010–13, or 39–57% of the expected deaths nationally. Elimination of measles deaths in India is feasible. The introduction of the measles vaccine in the 1960s led to large reductions in measles deaths in many countries. Yet, measles remains a major killer of children younger than age five worldwide, particularly among children living in Africa and Asia, where fewer children are immunized. India has been particularly hard hit, with annual child measles deaths exceeding 60,000 in 2005.In the 1990s, India’s national vaccination program made one dose of the measles vaccine part of routine vaccinations through much of the country to help reduce the numbers of measles deaths. However, it was one of the last countries to add a second dose of measles vaccine as recommended by the World Health Organization, which has been shown to prevent infection and death in 90-95% of vaccinated children.In 2008, the Indian government announced it would introduce a second dose of measles vaccine to its routine vaccine schedule for children from 2010 onwards. Prior to the introduction of a second-dose measles vaccine, campaigns were launched to increase immunization rates in regions where few children were being vaccinated. But how many young children’s lives were saved by these campaigns was unknown.Now, Wong et al. show that India’s measles immunization campaigns saved the lives of 41,000 to 56,000 children between 2010 and 2013. This averted between 39-57% of the expected measles deaths nationally during that time period. Wong et al. used data from the Million Death Study, which used household surveys to capture information on the cause of 27,000 child deaths in India between 2005 and 2013, to assess the affects of the state vaccination campaigns on measles deaths. Changes in measles deaths were compared to changes in unrelated child deaths to make sure any differences were related to the vaccination campaigns and not other improvements in children’s health care.Indian states with measles immunization campaigns saw larger decreases in measles deaths among children younger than five than states without such a campaign (27% vs 11%). Similar decreases were not seen in child deaths from other causes. Girls, who are disproportionately affected by measles in India, benefited from larger reductions in deaths than boys in states with immunization campaigns. The success of two-dose measles vaccination campaigns at reducing young children’s deaths in India may help to boost vaccination rates and help combat parents’ hesitance to immunize their children. Measles remains an important cause of death among under-five children . Much ofThe role of national intervention strategies in explaining the decline in measles deaths in India is unknown. In 2005, the Government of India launched the National Rural Health Mission – a program geared towards improving public health infrastructure and reducing child mortality in priority states . In 2008The ideal method of evaluation, a randomized trial, was not practical in the rollout of the national campaign. Mathematical models estimate an 84% decline in measles deaths globally during 2000–2016, but are unable to evaluate the effectiveness of interventions . In thesFrom 2005 to 2013, the MDS captured deaths for 13,490 girls and 13,007 boys aged 1–59 months after excluding children missing cause of death (2.8%). Of the 1,638 measles deaths using the definition of one or more physician coding or the family reported a measles history for the deceased, 79% occurred in rural areas, 73% in campaign states, 59% at ages 12–59 months, and 57% in girls . 76% of Annual measles deaths at ages 1–59 months fell from 62,000 to 24,000 from 2005 to 2013 . Prior tMeasles mortality rates among 1–59 month-old children in campaign states declined significantly following campaign launch  when comslope of six-month measles mortality rate per thousand live births and changes in the level (which were few). Prior to campaign launch, the slope of measles mortality in campaign states remained unchanged at –0.004 deaths per thousand live births . However, we observed a significant decrease in level of measles mortality following campaign launch . In all other stratified analyses, we observed no significant change in level  improved from 2002 to 2014, particularly in campaign states . In diffAmong 26,505 overall child deaths at 1–59 months for the whole of India from 2005 to 2013, the odds of measles mortality were higher at 12–59 months  than at ages 1–11 months, after adjusting for covariates identified in the above difference-in-difference analyses . ChildreConservatively, we estimate that the national campaign averted 41,000 to 56,000  child deaths in India during 2010–2013 . The majefficacy, preventing infection and death in 90–95% of children who receive two doses  as the cause of death or that the living respondent reporting a measles death noted a history of measles  . Though We conducted a multiple-group interrupted time series to assess the impact of measles campaign on 1–59 month measles mortality reduction. We arranged the data in a time series and divided the sample into time periods before and after campaign launch . We usedWe used multilevel logistic regression to examine characteristics of health-seeking behavior associated with measles mortality . We orgaWe constructed maps of 1–59 month measles mortality to determine the geographical distribution of measles mortality risk in India. We fitted the data using a generalized linear geostatistical model with integrated nested Laplace approximations. We adjusted for populations living in measles campaign districts and urban/rural residence. We used R version 3.5.1 for mapping.To estimate the magnitude of the intervention, we derived cumulative deaths differences using observed and expected measles mortality rates from the interrupted time series model. We extrapolated the expected rates using the pre-intervention trend from campaign launch to the end of the time series. We applied UN live births to their respective year and summed for the 2010–2013 period, then calculated deaths averted and percent averted between the observed deaths and the expected deaths. We report upper and lower bounds using the broad case definition, which captures one physician coding measles or family reporting of a history of measles, and the narrow case definitions, which captures only one physician coding measles. We used Stata version 15 for statistical analysis. In the interests of transparency, eLife includes the editorial decision letter and accompanying author responses. A lightly edited version of the letter sent to the authors after peer review is shown, indicating the most substantive concerns; minor comments are not usually included.eLife. Your article has been reviewed by three peer reviewers, including Mark Jit as the Reviewing Editor and Reviewer #1, and the evaluation has been overseen Eduardo Franco as the Senior Editor. The following individuals involved in review of your submission have agreed to reveal their identity:; Narendra Arora (Reviewer #2); Anindya Bose (Reviewer #3).Thank you for submitting your article "The impact of measles immunization campaigns in India using a nationally representative sample of 27,000 child deaths" for consideration by The reviewers all agreed that this is an important paper, using a large and rich dataset to demonstrate the strong impact of measles SIAs in India, as well as yielding a lot of valuable insights about measles epidemiology in that country. Overall, we would like to see this paper published, but there are a few aspects of the analysis and results that need some explanation.1) The analysis was limited to measles in children under 5 years. Some commentary on the appropriateness of this would be useful, since the target age range for the SIAs was up to 10 years. What% of measles deaths are in >5s – these might be particularly important in better performing states and hence may make the highest burden states look worse than they are in comparison.2) Figure 2: The interrupted time-series with non-measles controls is not very convincing because even the non-campaign states experience a much faster decline in measles deaths than control deaths. This suggests that measles mortality is decreasing faster overall than mortality due to the control diseases, and a difference-in-difference analysis is more reliable.3) Given the temporary and likely artifactual rise in measles mortality reported in 2009 , was this year also be taken out of the other analyses e.g. those in Table 1, Table 2 and Figure 1? Otherwise it may cause the downward slope of the pre-SIA trend to be underestimated. This is particularly important in the analysis in Table 2 which is used to calculate the overall measles deaths prevented.4) The Discussion section shows that WHO estimates of measles mortality in India are much higher than those estimated from MDS data. This is quite an important statement and if true may imply that there are shortcomings in the WHO methodology that may require revisiting measles mortality estimates in other countries also. Could this be explored a bit further e.g. what are the differences in WHO methodology that may have led to this difference?5) In Table 2, figures indicating the pre and post campaign state would be useful in interpretation at state level.6) In subsection “Coverage of Measles Immunization and Related Health Indicators”, source of the measles coverage data and whether for one dose or two doses needs to be specified.7) The MSL campaign starting 2010 targeted 9 months -10 years age group. This article purports to show that the campaign had an impact on measles deaths in 1-59 month old children in 2009/2010 to 2013. However, the cohort that was <9 months old in 2010, 1 year old in 2011, <2 years old in 2012, <3 years old in 2013 etc. would not have received any vaccination though SIA. So the effects of the campaign observed in this age group would be the indirect effect of the campaign by reducing transmission through sudden increase of herd immunity in cohorts born from 9 months before start of campaign. This indirect effect should be mentioned when we ascribe impact on mortality to immunization delivered through the campaign. This could also be the key underlying epidemiological mechanism in campaign states vs. non-campaign states where no such sudden increase in herd immunity occurred.8) Cause of death ascertainment from measles should not have taken MCV vaccination status into account. This should be mentioned explicitly as this can introduce a bias in the results.One of the reviewers pointed out that there is now a measles technical advisory group in India. Hence it may be appropriate to share this paper with them, with the Ministry of Health India and with WHO SEARO office, given the interest in the subject by these groups. 1) The analysis was limited to measles in children under 5 years. Some commentary on the appropriateness of this would be useful, since the target age range for the SIAs was up to 10 years. What% of measles deaths are in >5s – these might be particularly important in better performing states and hence may make the highest burden states look worse than they are in comparison.Measles deaths among children aged 1–59 months comprised 84% (1638/1958) of children up to 10 years of age in our overall sample. This proportion did not differ greatly between states. We have added the following to the Materials and methods section:“Though the campaign targeted children up to 10 years of age, our analysis focuses on children aged 1-59 months who comprised 84% (1638/1958) of these deaths.”2) Figure 2: The interrupted time-series with non-measles controls is not very convincing because even the non-campaign states experience a much faster decline in measles deaths than control deaths. This suggests that measles mortality is decreasing faster overall than mortality due to the control diseases, and a difference-in-difference analysis is more reliable.We agree that, visually, the measles mortality rate appears to decline faster than control mortality rates in non-campaign states. Using the interrupted time series model, we adjusted for differences in the baseline mean and time trends between measles and control mortality. Measles mortality in campaign states saw a significant decline after campaign launch (P = 0.040), whereas measles mortality in non-campaign states did not change after campaign launch (P = 0.157).We agree that to evaluate change in measles mortality following the intervention, the trends in measles and control mortality prior to the intervention must be parallel. For all models, we compared the pre-intervention trends in measles deaths to that of control deaths and found no significant differences. We have added the results of the pre-intervention trend comparisons to the footnotes in Figure 2 and Figure 4.Figure 2: “We observed no significant difference when comparing pre-intervention trends for the control deaths to the deaths from measles, pneumonia, or diarrhoea in the campaign states, or to measles deaths in the non-campaign states .”Figure 4: “We observed no significant difference when comparing pre-intervention trends for the control deaths to those for case deaths based on the narrower definitions of at least one physician coding measles and both physicians coding measles (P > 0.4 for both comparisons).”3) Given the temporary and likely artifactual rise in measles mortality reported in 2009 , was this year also be taken out of the other analyses e.g. those in Table 1, Table 2 and Figure 1? Otherwise it may cause the downward slope of the pre-SIA trend to be underestimated. This is particularly important in the analysis in Table 2 which is used to calculate the overall measles deaths prevented.We understand the concern for potential underestimation of the pre-intervention trends. We were cautious to ensure the estimates are conservative. We tested removal of the temporary rise in measles mortality in 2009 and saw no difference in pre-intervention slopes (P = 0.891). The narrower case definitions did not show a rise in measles mortality in 2009 as they did not include family-reporting of measles history. For these more narrow definitions, pre-intervention trends in measles deaths and control deaths did not differ significantly (see reply to Comment #2 above). We updated the Materials and methods section as follows:“The pre-intervention trends excluding 2009 deaths did not differ from control deaths (P = 0.9).”4) The Discussion section shows that WHO estimates of measles mortality in India are much higher than those estimated from MDS data. This is quite an important statement and if true may imply that there are shortcomings in the WHO methodology that may require revisiting measles mortality estimates in other countries also. Could this be explored a bit further e.g. what are the differences in WHO methodology that may have led to this difference?https://www.who.int/immunization/monitoring_surveillance/burden/vpd/WHO_SurveillanceVaccinePreventable_11_Measles_R2.pdf). We updated the Discussion section as follows:Thank you for raising this important point. Our measles case definition differs from the WHO definition which relies on either clinician-suspected measles infection or a diagnosis of fever with rash and cough, runny nose, or red eyes . The addition of possible measles deaths with rash and fever to our original case definition raised the estimate of measles deaths in 2013 from 24,000 to 46,000. At our observed rate of decline, we would expect 35,000 deaths in 2015 using the WHO definition. Further investigation of the reasons for these differences in total deaths from measles, particularly at the subnational level, is required.”5) In Table 2, figures indicating the pre and post campaign state would be useful in interpretation at state level.We are not able to reliably estimate AARR by pre- and post-intervention time periods by state due to low numbers. We updated now Table 2 to include AARR estimates for large states in India and by campaign/non-campaign states. We also added pre-intervention and post-intervention AARRs by campaign and non-campaign states to the footnotes. We updated Figure 1 to include pre- and post-intervention AARRs by sex, campaign states, and urban/rural residence, and India overall.6) In subsection “Coverage of Measles Immunization and Related Health Indicators”, source of the measles coverage data and whether for one dose or two doses needs to be specified.eLife format, we added the definition of measles vaccination coverage to the Results section, Materials and methods section and relevant figures as follows:To clarify the data sources and measles coverage definition in the Results section: “National measles immunization coverage (defined as the percentage of children aged 12 to 23 months receiving any dose of measles vaccine) improved from 2002–2014, particularly in campaign states .”Materials and methods section: “We also adjusted for state-level coverage estimates of measles vaccination , vitamin A supplementation, oral rehydration supplementation, maternal literacy, and treatment-seeking for diarrhoea and pneumonia.”Figure 5: “Estimates were obtained from the National Family Health Survey and the District Level Household and Facility Survey through 2002 to 2014. Measles vaccination coverage was defined as the percentage of children aged 12 to 23 months receiving any measles vaccine.”7) The MSL campaign starting 2010 targeted 9 months -10 years age group. This article purports to show that the campaign had an impact on measles deaths in 1-59 month old children in 2009/2010 to 2013. However, the cohort that was <9 months old in 2010, 1 year old in 2011, <2 years old in 2012, <3 years old in 2013 etc. would not have received any vaccination though SIA. So the effects of the campaign observed in this age group would be the indirect effect of the campaign by reducing transmission through sudden increase of herd immunity in cohorts born from 9 months before start of campaign. This indirect effect should be mentioned when we ascribe impact on mortality to immunization delivered through the campaign. This could also be the key underlying epidemiological mechanism in campaign states vs. non-campaign states where no such sudden increase in herd immunity occurred.We have updated the Discussion section as follows:“The observed reduction in under-five measles mortality may show herd immunity in cohorts born within nine months of the campaign launch.”8) Cause of death ascertainment from measles should not have taken MCV vaccination status into account. This should be mentioned explicitly as this can introduce a bias in the results.The proportion of respondents reporting no measles vaccination when both physicians assigned measles as the cause of death was 50% and did not differ greatly with the broader case definitions and controls (Table 1—source data 1). We updated the Results section as follows:“Despite inherent misclassification that can be expected from verbal autopsies, we observed that the proportion vaccinated against measles did not differ across case definitions, suggesting that physician assignment of deaths was not unduly biased by a history of measles vaccination (See source data for Table 1).”One of the reviewers pointed out that there is now a measles technical advisory group in India. Hence it may be appropriate to share this paper with them, with the Ministry of Health India and with WHO SEARO office, given the interest in the subject by these groups.Thank you. We are in collaboration with the Indian Council of Medical Research and have shared preliminary results. We will be holding dissemination sessions with collaborators and relevant organizations, in addition to working with the WHO on validation of national and subnational estimates.


After 53 years of quiescence, Mount Agung awoke in August 2017, with intense seismicity, measurable ground deformation, and thermal anomalies in the summit crater. Although the seismic unrest peaked in late September and early October, the volcano did not start erupting until 21 November. The most intense explosive eruptions with accompanying rapid lava effusion occurred between 25 and 29 November. Smaller infrequent explosions and extrusions continue through the present (June 2019). The delay between intense unrest and eruption caused considerable challenges to emergency responders, local and national governmental agencies, and the population of Bali near the volcano, including over 140,000 evacuees. This paper provides an overview of the volcanic activity at Mount Agung from the viewpoint of the volcano observatory and other scientists responding to the volcanic crisis. We discuss the volcanic activity as well as key data streams used to track it. We provide evidence that magma intruded into the mid-crust in early 2017, and again in August of that year, prior to intrusion of an inferred dike between Mount Agung and Batur Caldera that initiated an earthquake swarm in late September. We summarize efforts to forecast the behavior of the volcano, to quantify exclusion zones for evacuations, and to work with emergency responders and other government agencies to make decisions during a complex and tense volcanic crisis. Third, lack of instrumental monitoring prior to previous eruptions meant that there was no geophysical monitoring basis for interpreting the somewhat unusual sequence of unrest. In this article, we document the volcanic unrest and eruption (still continuing), and provide a summary of actions taken by our group from the Indonesian Center for Volcanology and Geological Hazard Mitigation (CVGHM) and the USGS-USAID Volcano Disaster Assistance Program (VDAP), and by our colleagues, and other government officials in an effort to mitigate the impacts of any potential volcanic eruption.In August 2017, Mount Agung displayed increasing unrest, manifested as anomalous heat flow and felt earthquakes. The potential for activity at Agung was daunting for three primary reasons. First, this enormous ~3000-m-high volcano looms above the nearby landscape, threatening nearly 200,000 people who live within the hazard zone. Second, the previous eruption in 1963 killed over 1000 peopleMount Agung is sacred to the Hindu population of Bali, and is a great tourist attraction on the island. The volcano also supports intense agriculture, as abundant cold springs issue on its south flank at elevations of 300–500 meters. The springs form the basis for the Balinese subak system of water distribution that supports irrigation of terraced rice paddies on the volcano’s lower slopes. Higher up, deposits of volcanic ash and lapilli are quarried for the aggregate needed to support development of urban growth near the Balinese capital city of Denpasar and the tourist destinations of Ubud and Kuta. Mount Agung clearly supports the region economically, but it also poses great threats, as was made apparent during the last eruption in 1963.1. Fire fountaining of lava within the summit crater resulted in accumulation of lava and eventual effusion through a low-point in the crater wall, traveling 7.5 km down the north flank. Pyroclastic density currents (PDCs) resulted in the eruption’s first fatalities only a day later2. On 17 March 1963, a month after the initial felt earthquakes, a VEI 5 paroxysm created a 19- to 26-km-high plume with associated 10.5 to 14-km-long pyroclastic density currents and follow-on hot and cold lahars, which together killed over 1000 people2  was consistent with rapid rise of volatile-rich mafic magma4. Notably, in September of 1963, after ~40 years without eruption, basalt and basaltic andesite lava issued from a cone in the nearby Batur Caldera . Although many hypocenters were also manually computed using the CVGHM network during the crisis, these were used primarily to verify and supplement BMKG solutions and were not consistently catalogued. The description of activity below is a brief summary of the observed seismicity from all data sources.A swarm of earthquakes M2.3-3.9) was recorded in mid-May 2017, located NW of the Batur caldera, with a maximum reported intensity of MMI III. After several months of gradual increases, earthquake rates and seismic energy increased rapidly between 16 and 22 September 2017 from tens of earthquakes per day to hundreds of earthquakes per day  that suggests these episodes were related to rainfall at the summit, plausibly due to interaction of rainfall with scalding rock by means of growing cracks in the crater lava. During the most intense phase of the eruption, transit of lahars was seismically recorded on the N and S flank of the volcano. These lahars were thought to have originated by rainfall on ash that was deposited on the upper flank of the volcano during the initial explosive activity during the period of approximately 21–30 November.After the most intense phase of eruptive activity in late November, seismicity decreased. Though rate increases in LF seismicity culminated in Strombolian-type explosions on 19 January 2018, and large (M3+) VTs continued in February and March, overall earthquake rates decreased to tens of events per day or fewer. On 23 June 2018, a small swarm of VT and LF seismicity began and increased until an explosion on 27 June and additional lava extrusion and ash emissions on 28–29 June, which was accompanied by monochromatic tremor. On 2 July 2018, Strombolian activity was recorded as a series of seismic explosion signals. Seismicity associated with intermittent explosive activity continued through the present (June 2019).9 of the displacements suggests a pressure increase at 10–20 km depth, although the few data points do not permit a more detailed assessment. The deformation is not apparent in InSAR data spanning the time period, probably due to the small magnitude of the displacements10.Deformation of Mount Agung is monitored by a network of 5 continuous GNSS stations Fig.  that was10 while GNSS stations—particularly REND—are consistent with a combination of dike intrusion to the northwest of Mount Agung and deflation of a deeper source (the same source that inflated in February–March and August–September). A co-eruptive episode of deformation in November 2017 coincided with the onset of lava extrusion and is consistent with deflation of a source beneath Mount Agung, although the data cannot distinguish the depth of this source. From mid-December 2017 through April 2018, surface deformation was minor. From May to mid-June 2018 shallow inflation was detected, followed by extrusion of lava and an increase in explosion frequency in late-June to July 2018.The rapid increase in seismicity in September was accompanied by a significant change in deformation at all sites Fig. . Station2 in bulk composition of the erupted ash from 22 November 2018 to 29 November 2018. Semi-quantitative analyses of juvenile glass confirmed an andesitic composition. A small lava flow was first observed within this crater on 25 November and by 27 November had covered the crater floor  using ground-based remote sensing techniques began in October 2017 after steam emissions had visibly increased. Despite the presence of a small, persistent plume and reports of sulfurous-smelling gases from unauthorized hikers, 12 mobile DOAS 11 campaigns made between 1 October and 14 November 2017 all failed to detect SO2.Due to the previous lack of long-lived fumaroles at Agung since its 1963 eruption, no geochemical monitoring program or instruments were in place prior to the 2017 unrest. Conditions near the summit were considered too hazardous for proximal sampling, so regular attempts to measure sulfur dioxide  to obtain airborne in situ measurements of plume H2O vapor, CO2, SO2, and H2S. The drone was launched from 530 m elevation at a location 11 km south of the summit and climbed to ~3,300 m for sampling ; SO2 was below the sensor detection limit  yielded an SO2 emission rate of 660 t/d , very low SO2 mixing ratios , and trace H2S  instrumented with a miniaturized multi-GASing Fig. . The firt/d Fig. . Three dber Fig. .Figure 62 emission rate was measured on 26 November  but quickly fell to 180 t/d by 1 December. Gas emissions during lava effusion in December were highly variable  and magmatic in character; a drone flight on 19 December intercepted a dense plume with clear H2O, CO2, and SO2 peaks . The DOAS measurements picked up very low levels of BrO in the large 26 November plume (BrO/SO2 = 3E-5). Subsequent data showed an increasing trend up to BrO/SO2 = 1.8 and 1.9E-4 detected on 17 and 18 December, respectively . SO2 emissions briefly jumped to more than 1000 t/d in the week after the 28 June 2018 extrusion event, but then quickly returned to low baseline values (<200 t/d) by the beginning of August 2018.The highest SOely Fig. . The incBelow, we provide a timeline of events as they occurred, though in some cases, as with deformation, they were not detected at the time. We frame the timeline in terms of alert-level changes so that the reader can appreciate the events and reasoning that led to those changes. The date of alert-level change is denoted in the header for each entry, though key events and observations begin prior to and after that date.14 September 2017–– Upgrade to Level 2: The first swarm of earthquakes was recorded by the local Agung and Batur seismic network in mid-May 2017. Figure 10. Unusual fumarolic activity in the northeast part of the summit crater, along with increasing seismicity, prompted an alert level change to Waspada (Level 2) on 14 September  was noted in the crater on 14 September and formed small deltas in the vicinity of the fumaroles. Increasing fumarolic activity, a growing thermal anomaly in the crater, and felt earthquakes (M3+) increased the level of concern of local populations. Rapidly increasing seismicity prompted an alert level change to Siaga (Level 3) on 18 September.22 September 2017–– Upgrade to Level 4: Seismicity continued to accelerate rapidly and RSAM values peaked on 22 September . With the decrease in seismic event rates and the long (one-month) duration of evacuations, the alert level was lowered to Siaga (Level 3) on 29 October. In early November, RSAM values began to increase slowly  was detected the following day by mobile DOAS, consistent with magma degassing  satellite. Lightning, loud rumblings, and lahars were produced  was limited to the Mount Agung crater, and there was no evidence for changes at Mount Abang or Batur caldera . Perhaps an intrusion beneath Mount Agung had seismically activated faults NW of the summit through pressurization of groundwater , or due to removal (scrubbing) of SO2 during magma ascent, such that originally CO2- and SO2-rich gas was depleted in SO2 before reaching the surface23. For magma to ascend 10 km in the 9 hours between gas measurement and eruption requires rapid ascent at >30 cm/s (0.01 MPa/s). These rates are commonly associated with large explosive eruptions24, whereas the eruption on 21 November was a small-volume phreatomagmatic event. For this reason, we favor interpretation of extensive scrubbing during shallow magma residence prior to eruption.However, the best-fit source models for InSAR deformation dataure Fig. . In thiskes Figs  and 7B. 2 emission rates were high on 22 November (660 t/d) but waned rapidly. Plume CO2/SO2 increased by an order of magnitude from 23-24 November (77 to 824) but this change in the ratio was due to lower plume SO2 abundances, rather than an increase in CO2. Most likely, the pathway for magma degassing was temporarily opened after the explosions on 21 November, but soon closed so that gas again traversed the extensive groundwater system with its capacity to dissolve SO2. The process repeated on November 25 when renewed phreatomagmatic explosions gave way to magmatic explosions and gases rich in SO2 were again measured. The eruption plume heights peaked on 27 November, coincident with very high lava effusion rates, reaching 36 m3/s over the interval 25–29 November. Peak SO2 emission rates were measured during this interval as well, with 5,500 t/d measured on 26 November. Lava effusion slowed dramatically by 30 November, but small explosive eruptions continued, venting through surface lava such that they were not recorded seismically. By 23 December, the character of these explosions had changed. Explosions began to produce permanent pits in the lava surface and were detectable seismically, a change that we interpret to reflect rheological stiffening of surface lava due to cooling and coincident solidification. Furthermore, large decreases in SO2 emission rates and increases in BrO/SO2 after late November were consistent with diminishing eruption rates through time (to 230 t/d average in January and 220 t/d in February), and a dominance of shallow degassing.Following the first phreatomagmatic eruptions, SOc.f. ref.20) followed by an additional similar intrusion in late August to mid-September 2017. Both events were evident with inflationary signals in GNSS data, but neither was detected with InSAR interferograms. The initial deep intrusion was aseismic, though the second was accompanied by some VT seismicity.Early magma ascent in February – March 2017 from a deep crustal reservoir  and locations . We speculate that the M4.9 earthquake on 8 November marked the culmination of pressurization due to the intrusion, and that magma later was able to progress upward through a more permeable pathway toward the surface. Toward the end of the period 12–22 November, magma reached shallow depths (<1–2 km) and CO2 emissions were measured. On 25 November a small lava flow was detected in the crater, coincident with continued explosions. By the end of November, a 24 million m3 lava flow filled about one-third of the crater volume. Ash-rich explosions through the lava continued over subsequent months. Initially, cracks and explosion craters that formed in the flow were filled with lava, but as the flow stiffened with time, subsequent cracks remained unhealed. Over time, as the magma eruption rate declined, so did gas emissions.The eruption began on 21 November with phreatomagmatic explosions, and high SOUsing this framework, we now favor a conceptual model that includes dike emplacement and involves several stages of magma ascent:26. All event trees and their supporting data are presented in the Event-Tree Supplement. Each of these short-term, event-tree forecasts covered a period of two weeks, because this relatively short time interval was of relevance to the emergency managers. Early event-tree eruption forecasts (late September and early October) relied heavily on seismic data, remote sensing observations, analysis of past eruptive periods at Agung, and comparisons with analog volcanoes and analog seismic progressions. Table To assist with forecasting and providing advice to emergency managers, we utilized probabilistic event trees eight times during the Mount Agung crisis: on 20 September, 23 September, 2 October, 17 October, 15 November, 11 December, 24 January, and 12 March (Table As the crisis continued (mid-October to November), additional monitoring streams aided short-term forecasting efforts, including the GNSS data retrieved in early to mid-October and initial InSAR data that were processed, corrected, and interpreted by mid-November. Eruption likelihoods were estimated at 50% and 40% on 17 October and 15 November, respectively, as time since peak seismicity increased and the possibility of waning activity was increasingly considered. However, uncertainty (±20%) also increased over previous trees due to slow rates of change in seismicity and increasing duration of unrest without eruption. Conditional probabilities for eruption magnitude remained the same as in earlier trees because their basis remained the same, but were subdivided, placing VEI 0–1 at 45%, VEI 2 at 25%, VEI 3 at 20%, and VEI 4–5 at 10%. The 15 November tree further highlighted a phreatic eruption scenario, which was deemed to be the most likely (80%) conditional eruptive scenario. Both trees preceded the addition of drone-assisted multi-GAS geochemical measurements, the results of which would have increased expert estimation of eruption probability, and the first of which was rapidly followed by eruptions on 21 November.28, energy cone models, and past runout of PDC’s during the 1963 eruptive sequence. Due to the large size and steep slopes of the Agung edifice, even relatively small volume eruptions can produce PDCs with far-travelled runout. Additional information is available in the Event-Tree Supplement).For all of these event trees, forecasts of PDC runout relied on comparisons with global data from the FlowDat databaseOur ability to forecast upcoming activity was initially limited by the number of operational seismic and GNSS stations, the hazard in climbing to the summit to make observations, and clouds and rain inhibiting robust DOAS measurements of the low gas output. Over time, these challenges were solved by addition of seismic and GNSS stations, the use of drone-assisted multi-GAS measurements, radar satellite surveillance, and event trees. InSAR, though highly important toward our current understanding, was a challenge to use during the crisis because of the relatively small geodetic signal, and the requirements for careful subtraction of tropospheric humidity effects on the radar signal that introduces latency between data collection and confidence in the processed data sufficient to inform hazards assessments.29 range from minutes to months in mafic systems. We suggest that a careful evaluation of delay times compared to eruptive outcomes may help inform the most appropriate time interval over which short-term forecasts should be made.Another key aspect of unrest at Mount Agung was the long two-month interval between peak seismicity and eruption, which clearly affected the results of the group forecasts through the event-tree exercise Table . What ad10. Is it possible that this difference was due to magma ascent-rate variation, where final ascent was slower than transfer into the inferred 10-km-deep dike? Or, is it possible that the permeabilities and rheologies of the pathways are sufficiently different to account for the variation in seismic behavior?Finally, we are still puzzled by the relative lack of shallow seismicity during final ascent compared to intrusion of the inferred dike at ~10 km depth7; (2) the large population within the hazard zones ; (3) the high population density of eastern Bali; (4) Bali’s importance as a domestic and international tourist and business conference destination; (5) the presence of important agricultural and aggregate resource areas on the flanks and upper slopes of Mount Agung; (6) fifty years of dormancy at Mount Agung accompanied by minor eruptions at the nearby Batur caldera; (7) a majority population rooted in the stoic and ritualistic philosophy of Balinese Hinduism; and (8) the relatively long run-up time of ten weeks between initial felt earthquakes and magmatic eruption.A number of factors contributed to intense social and political pressure on those involved in the response to the 2017-2018 crisis. These factors include: (1) a history of large eruptions at Mount Agung30, yet such a linkage has been practiced for decades in Indonesia and has saved many tens of thousands of lives33. The Agung crisis is an example of how, despite intense social and political pressure, through coordinated actions of the volcanologists at CVGHM and members of the Indonesian emergency management community, mitigation actions were taken that effectively protected the at-risk population from what could have been a major disaster.It has been argued that linking alert levels to mitigation actions can result in volcanologists’ decisions being too affected by social and political forces34, in which a volcano observatory issues warnings and acts as the primary advisor to the emergency managers. In Indonesia, CVGHM is responsible for both alert levels and hazard zonation maps; whereas, the Badan Nasional Penanggulangan Bencana  along with Badan Penanggulangan Bencana Daerah  and local government agencies use the CVGHM alerts and maps to prepare response plans. Evacuations are organized by these emergency managers and carried out by various combinations of national and local military and police agencies and by community leaders. Implementation of mitigation policies, strategies and actions by governments and communities are directly tied to the four alert levels  is whether there are indications that eruptions could potentially threaten populations near the volcano. This distinction, which clearly goes beyond the capabilities of volcanologists alone30, requires an effective partnership between CVGHM and the Indonesian emergency management and political community. Although the primary factor in decisions to change the alert level at Agung was the state of volcanic unrest as determined by CVGHM, many other factors related to the potential threat to population were considered by the emergency managers. There was considerable pressure on CVGHM to lower the alert level following the initial decline in seismicity in early October through the first eruption, and during the extended period December 2017–early February 2018  exclusion zone for Level 4 implemented on 22 Sept. Fig. . The relhttps://magma.vsi.esdm.go.id/) and optimized for mobile smart phones, provides rapid communication of a wide range of geological hazards information from CVGHM to the public. It disseminates varied information, including volcano alert levels, observation reports with hazard warnings and mitigation recommendations, hazard maps, press releases, volcano observatory notice for aviation (VONA) alerts, and near-real-time monitoring data . The application also allows users to share information through various social media . Advisories for Agung were provided to the public, mainly in Indonesian, but also at critical times in English to meet demands for information to the international tourist community. The Agung volcano observers routinely used this application to disseminate volcanic activity reports to the communities and stakeholders through tens of WhatsApp (a messaging application) groups. WhatsApp was the most used messaging application for most Indonesians to share information at that time.The increasing activity of Mount Agung reinforces our experience that public communication is critical during crises. CVGHM extensively used the MAGMA Indonesia application as a tool to disseminate information during the Mount Agung crisis . The application, which is accessible on the internet (Orari) Using the Orari system, warnings of unrest that exceeded predefined parameters could be issued immediately, even by a single observer at the Agung Observatory in the middle of the night. The messages used a simple single-digit numbered system to advise on the extent of the hazard. For example, the simple coded message “Warning #4” indicated a hazard of PDC’s that could potentially extend to a radius of 18 km in predefined sectors of the volcano. Crisis response officials in all communities on the flanks of the volcano routinely use radios to communicate, therefore the radio system was a familiar and accepted means to receive hazard warnings. The officials receiving the messages were trained about the hazards and how to respond, and they had maps showing evacuation zones for each level of instant warning.An innovative multiplex radio-based system was used during Alert Level 4, in which “instant warnings” could be delivered simultaneously to the dozens of community officials on the flanks of the volcano. The radio system was provided by the Indonesian Amateur Radio Organization  to Waspada (Level II), people’s awareness started to increase. While most of the people were aware of the hazard warnings, most of them were not aware of how to respond to such warnings. Most people did not have sufficient knowledge regarding hazards and risks around the volcano, especially given that the volcano had been dormant for over fifty years. This was shown during the rapid changes in alert levels from Waspada (Level II) to Siaga (Level III) on 18 September and from Siaga (Level III) to Awas (Level IV) on 22 September, resulting in a chaotic situation for the community around the volcano. Moreover, false information was circulated amongst the local  population, stating that an eruption was imminent. People hurriedly evacuated and were stuck in traffic for tens of hours on their way to evacuation camps. Some felt the need to sell their livestock at a loss. Some people unnecessarily self-evacuated based on their understanding of the events of 1963, and not based on the actual exclusion zones. Although only around 70,000 people from 27 villages were supposed to be located in the evacuation camps, an additional >70,000 people from 51 villages self-evacuated from outside of the recommended exclusion zone. This created a more expensive and chaotic situation that added to the challenge for public officials and emergency response agencies.When the eruption did not occur soon after the alert level was first elevated to the highest level (Awas), there was a growing opinion that the CVGHM volcanologists had made a mistake in analyzing the activity. After the alert level was downgraded and the exclusion zone was reduced in distance in October (6.0–7.5 km from the crater), some people came back to their homes and some believed that the volcano would not erupt. However, in one month the volcano eventually erupted, the alert level was upgraded, the exclusion zone was increased (8–10 km from the crater), and some people had to return to evacuation shelters, creating further confusion and frustration. Clearly, it is difficult to balance the level of concern in the local population with the perceived risk by the volcano observatory, and with the mitigation responsibilities of the many relevant local and national governmental agencies.In order to relieve tensions and to increase people’s understanding related to Mount Agung’s dynamic activities over the course of the crisis, CVGHM undertook a series of strategic actions. Balinese people, especially those living around Mount Agung, are deeply influenced by their culture and religion; furthermore, community and religious leaders have a key role in public communication. CVGHM responded by organizing regular direct discussions with these leaders at local government offices or at residences of community and religious leaders. As these meetings progressed over time, mutual trust was built. We found that these leaders had a decisive role in improving communications between CVGHM and the local community. Direct discussions with the community were also carried out in refugee shelters and were attended by several related stakeholders including local government agencies, Regional Disaster Management Agency (BPBD), army, police, etc. CVGHM often utilized native Balinese spokespersons to more fully relate to the local population. CVGHM scientists also gave hundreds of interviews to the news media. Although this required a major effort in communication by scientists and officials, these actions were an essential element in maintaining public awareness of the hazards, and in explaining that the volcanic hazards did not extend to the entire island of Bali.Despite the overall success of the crisis response to the eruption of Mount Agung, there were also significant challenges. These included: maintaining the necessary resources for evacuation and community training when the alert level was not at the highest, lack of explosive eruption experience among many residents of Bali, and competing opinions from non-government officials about the relative hazards as reported in the media.In order to maintain awareness about the hazards at Mount Agung and to mitigate the risks from future eruptions, several actions are warranted. Continual education of the local population is required to ensure that they are prepared to respond to changing events and to take recommended actions. Prior to September 2017, the local officials and religious leaders in Bali were relatively unfamiliar with the events of 1963 and the potential for renewed activity. Intensive meetings and discussions were required to allow for cooperative planning to aid in evacuation. In the future, periodic “table-top” exercises are recommended to help coordinating agencies maintain a system of communication and coordination in advance of future crises. These exercises could form a part of volcanic eruption contingency plans, which would necessitate periodic updates to reflect administrative and political changes. Although some residents remember the eruption of 1963, many others do not. Knowledge of the catastrophe was passed down through family stories, but were rarely, if ever, shared publicly. Therefore, emphasis must be placed on continuing education for local citizens and school children about volcanic hazards in the Agung area and the potential for future eruptions.And finally, land-use planners and governing authorities should focus additional efforts into consideration of volcanic hazard zones in land management and regulatory decision-making. By limiting the population living, working, or travelling through hazard zones, the volcanic risk can be mitigated during future volcanic crises in the area.Supplemental FiguresCover Sheet Event-Tree SupplementEvent-Tree Supplement 1Event-Tree Supplement 2Event-Tree Supplement 3Event-Tree Supplement 4Event-Tree Supplement 5Event-Tree Supplement 6Event-Tree Supplement 7


Hepatitis E Virus (HEV) is the leading cause of acute viral hepatitis globally. Symptomatic infection is associated with case fatality rates of ~20% in pregnant women and it is estimated to account for ~10,000 annual pregnancy-related deaths in southern Asia alone. Recently, large and well-documented outbreaks with high mortality have occurred in displaced population camps in Sudan, Uganda and South Sudan. However, the epidemiology of HEV is poorly defined, and the value of different immunisation strategies in outbreak settings uncertain. We aimed to estimate the critical epidemiological parameters for HEV and to evaluate the potential impact of both reactive vaccination (initiated in response to an epidemic) and pre-emptive vaccination.We analysed data from one of the world's largest recorded HEV epidemics, which occurred in internally-displaced persons camps in Uganda (2007–2009), using transmission dynamic models to estimate epidemiological parameters and assess the potential impact of reactive and pre-emptive vaccination strategies.Under baseline assumptions we estimated the basic reproduction number of HEV in three separate camps to range from 3.7  to 8.5 . Mean latent and infectious periods were estimated to be 34  and 40  days respectively.Assuming 90% vaccine coverage, reactive two-dose vaccination of those aged 16–65 years excluding pregnant women (for whom vaccine is not licensed), if initiated after 50 reported cases, led to mean camp-specific reductions in mortality of 10 to 29%. Pre-emptive vaccination with two doses reduced mortality by 35 to 65%. Both strategies were more effective if coverage was extended to groups for whom the vaccine is not currently licensed. For example, two dose pre-emptive vaccination, if extended to include pregnant women, led to mean reductions in mortality of 66 to 82%.HEV has a high transmission potential in displaced population settings. Substantial reductions in mortality through vaccination are expected, even if used reactively. There is potential for greater impact if vaccine safety and effectiveness can be established in pregnant women. Hepatitis E virus is a leading cause of acute viral hepatitis in developing countries. About 20% of those infected develop clinical symptoms; of those, about 2% of non-pregnant cases and 20% of pregnant cases die. There is a safe and effective HEV vaccine that is licensed in China for those aged 16–65 years who are not pregnant. The potential for using this vaccine in outbreak settings has not previously been examined. We analysed data from one of the world’s largest recorded HEV epidemics. We estimated that one case infects, on average, between 4 and 9 others at the start of an epidemic. We found that vaccination restricted to those aged 16–65 who are not pregnant could reduce mortality in outbreak settings by between about 10 and 30% if used reactively (initiating vaccination after the start of an epidemic); pre-emptive vaccination of the same group could reduce mortality by 35–65%. Substantially higher reductions in mortality are likely if vaccination can be safely extended to pregnant women and other age groups without loss of effectiveness. However, even if this is possible, reactive vaccination is unlikely to reduce mortality by more than 50% while pre-emptive vaccination can reduce mortality by 80 to 100%. Communicable diseases are responsible for large excess mortality and morbidity in complex emergencies . EpidemiThe risk of symptomatic illness given HEV infection is thought to increase with age and has been estimated to be about 20% in adults infected with genotypes 1 or 2 ; the proWith over 60 million people living as refugees or otherwise forcibly displaced worldwide , the majKnowledge of the course of HEV infection is based mainly on two volunteer studies ,14 and tTransmission of HEV is widely thought to occur predominantly via the faecal-oral route, usually through contact with contaminated water; it might therefore be assumed that household person-to-person transmission is rare. However, a case-control study of 112 symptomatic cases and 145 controls in Paloga, Uganda found only two behavioural risks associated with symptomatic HEV infection (with adjusted odds ratios of 3 and 2 respectively): use of wide-mouthed water storage vessels and communal hand washing . DrinkinThere is a recombinant vaccine, HEV 239, which is approved for use in China in those aged 16–65 years who are not pregnant . ClinicaWe aimed to quantify key epidemiological parameters for HEV in IDP camp settings and evaluate the potential benefits of vaccination. We consider both pre-emptive (prior to HEV cases occurring) and reactive vaccination , and evaluate the potential impact of selecting different target groups to receive the vaccine.To do this we fitted dynamic transmission models to data from three large HEV outbreaks in IDP camps. We used a Bayesian framework to combine data from previous studies with observed epidemic data to obtain an improved understanding of the natural history of HEV infection, quantify the transmission potential, and evaluate the potential for vaccination to reduce the number of clinical cases and associated mortality.Data came from three outbreaks in 2007–2009 from IDP camps in the district of Kitgum, Uganda: Agoro, Madi Opei, and Paloga . These outbreaks were part of a larger epidemic in Kitgum district. Conditions in the camps were crowded and in all camps access to water and sanitation was initially poor . Though We fit a series of deterministic transmission models to the data. We assumed latent and infectious periods and the probability of infections being reported were common to all camps, as the demographics and provision of healthcare at the three camps was similar.We allowed transmissibility to vary by camp, as this might be expected to depend on local camp conditions . In our A human challenge study had previously reported that viable virus could be found in the faeces four days before onset of the icteric phase of disease (i.e. jaundice) . We therModel fitting was performed within a Bayesian framework using a Markov chain Monte Carlo (MCMC) algorithm to derive the posterior distributions for unknown parameters. For each model we used at least 4 million Markov chain iterations and assessed convergence by visual inspection of the trace plots.φj represents the set of unknown parameters for model j and if p(φj |D) is the posterior distribution of these parameters given model j, data D and priors p(φj) thenIf φj, a system of differential equations was used to determine the expected number of new infectious people in each seven-day period, Zi , we assumed that the observed number of cases in each camp in week i followed a negative binomial distribution, with a mean given by the product of Zi and π, the proportion of infections which are reported. In all cases parameters to be estimated included this proportion (π), the dispersion parameter of the negative binomial distribution, the time of the first case in each camp, the rates of leaving latently infected and infectious compartments, and a transmission parameter, β. Compared to the baseline model (Model 1), Models 2a and 2b estimated three additional parameter corresponding to the camp-specific estimates of the water and sanitation intervention effect on rates of transmission assuming stepwise effects associated with the intervention, and effects that scaled in proportion to the water sources per person respectively. This analysis was intended to evaluate the evidence that this intervention contributed to epidemic control.For the likelihood term, λ, the rate of increase in the contamination of the saturating environment per infected host and υ, the rate of loss of contamination from this environmental reservoir.Model 5 estimated one additional parameter . Model 6 estimated two additional parameters: To evaluate the potential impact of vaccination we used estimates of vaccine effectiveness after two and three doses derived from data in Zhu et al  Table 2Table 2. https://moru.shinyapps.io/HEVmodel/.Case fatality ratios amongst those pregnant and those not pregnant were derived from the meta-analysis of Rein et al. . We assuhttps://github.com/BenSCooper/HEVmodel.In all simulations we accounted for uncertainty in model parameters by drawing these from the posterior distributions obtained by fitting the model to data from the three camps. Analysis was performed in R . Model cThe baseline model (Model 1a) gave good fits to the three epidemic curves, with the observed number of weekly cases usually within the 95% prediction intervals for observed cases (grey shaded region) . Under tAnalysis of the data using models explicitly accounting for the water and sanitation intervention  was capable of producing important though relatively modest reductions in cases and deaths , top rowThe impact of excluding pregnant women from the population to vaccinate varied with scenario. In cases where vaccination had a high chance of achieving herd immunity (i.e. when vaccine was used pre-emptively without age restriction) excluding pregnant women had only a moderate negative impact on reductions in mortality. In contrast, when herd immunity was less likely to be obtained through vaccination (i.e. when the vaccine was used reactively or with age restrictions) excluding pregnant women led to substantially smaller reductions in total mortality .Comparing vaccination policies with and without age restrictions, restricting receipt of the vaccine to those between the ages of 16 and 65 years had a small impact on reductions in mortality in the reactive vaccination strategy, but a far larger negative impact on the pre-emptive vaccination strategy, a consequence of reducing the chance of achieving herd immunity in these latter strategies.These broad conclusions about the impact of different vaccination strategies were robust to precise details of model specification. In particular, under all 25 scenarios in the model with two modes of transmission (Model 6), similar patterns were seen , though 0,) exceeded 6.5 under all model assumptions. This number has important implications for vaccination policies. To achieve herd immunity requires successful immunization of a percentage of individuals given by 100-100/ R0 [0 value of 6.5, we would require 85% of the population to be effectively immunized to achieve herd immunity. Herd immunity is desirable as it means a major epidemic will not be possible even though many in the population remain susceptible to infection. It is of particular relevance here because HEV-infected pregnant women face a greatly increased risk of mortality but the safety and efficacy of the vaccine in pregnant women remains to be established.Our results indicate that in displaced population settings HEV can be highly transmissible. In one camp  the estimated mean number of secondary cases per primary case at the start of the epidemic , the benefits could be substantial, with reductions in mortality of over 40% likely under baseline assumptions given pre-emptive vaccination with three doses. A robust finding was that pre-emptive vaccination was much more effective at preventing HEV cases and deaths than reactive vaccination. Estimates of reductions in mortality were based on the case fatality estimates taken from the meta-analysis of HEV genotypes 1 and 2 outbreaks by Rein et al ,33. EvalOur work sheds light on other important aspects of HEV epidemiology: we found consistent evidence that the mean latent period is between about 20 and 40 days and that a little over 10% of individuals infected with HEV are identified as cases. The data were less informative about the mean infectious period, though were consistent with the range 20–70 days suggested by previous data.Previous epidemiological investigations had suggested that household-level person-to-person spread may have been important in this epidemic ,22. Our Given the uncertainties about transmission routes and fundamental methodological challenges in making inferences about an unobserved environmental reservoir , we perfStrengths of our work include the use of high quality epidemic data, extensive sensitivity analysis, and an analysis that allows us to incorporate information from previous HEV investigations. This work also has important limitations. First, estimates of HEV vaccine effectiveness from a trial in China may not generalise to a typical emergency setting or to age/ethnic groups not included in the trial. Moreover, the model assumes that when an individual is vaccinated they have an all-or-nothing response: either full protection against disease and ability to transmit infection or no protection. In practice, components of vaccine protection may be more complex , and it In conclusion, this work has shown a high transmission potential for HEV in displaced population settings, shed light on important natural history parameters, and has shown that mass vaccination campaigns in such high risk populations have the potential to lead to substantial reductions in mortality.In 2015 the Strategic Advisory Group of Experts on immunization could not recommend the routine use of the vaccine for population sub-groups including children aged less than 16 years and pregnant women but emphasized that the use of the vaccine during outbreaks of hepatitis E should be considered . Our finS1 Fighttp://www.spherehandbook.org/en/water-supply-standard-1-access-and-water-quantity/). The broken green line shows the start time of the water and sanitation intervention in the three camps. The bottom row shows the number of Hepatitis E cases reported in the three camps over the same period. For periods prior to the collection of data we assumed that the number of water sources per person was constant and equal to the first observed value in the same camp. Similarly, for periods after collection of water and sanitation data, we assumed that the number of water sources per person was equal to the most recent recorded value in each camp.The figure shows the number of water sources (taps) per person in each camp and the timing of the water and sanitation intervention relative to the epidemic curves. The top row shows the water sources per person from the three camps by week number (from January 2007). Absence of a black line segment indicates lack of data; the red dashed line corresponds to minimal Sphere recommendations of no more than 250 people per tap assuming a flow of 7.5 litres per minute ((TIFF)Click here for additional data file.S2 FigPriors p1 to p5 correspond to varying the initial percentage of transmission occurring via this environmental route between mean values of 5% and 95% when the population is fully susceptible. Priors w1 to w5 correspond to varying the persistence of viable virus in this environmental reservoir, with mean durations ranging from two days to eight weeks. Prior and posterior distributions shown correspond to the proportion of transmission that occurs via this environmental route in completed epidemics at Agoro .(TIFF)Click here for additional data file.S3 Fig(TIFF)Click here for additional data file.S1 Appendix(PDF)Click here for additional data file.S2 Appendix(PDF)Click here for additional data file.S3 Appendix(PDF)Click here for additional data file.S1 Table(PDF)Click here for additional data file.