Magnitude Of Outbreak Research Articles

Drivers and epidemiological patterns of West Nile virus in Serbia.

West Nile virus (WNV) is an emerging mosquito-borne pathogen in Serbia, where it has been detected as a cause of infection in humans since 2012. We analyzed and modelled WNV transmission patterns in the country between 2012 and 2023. We applied a previously developed modelling approach to quantify epidemiological parameters of interest and to identify the most important environmental drivers of the force of infection (FOI) by means of statistical analysis in the human population in the country. During the study period, 1,387 human cases were recorded, with substantial heterogeneity across years. We found that spring temperature is of paramount importance for WNV transmission, as FOI magnitude and peak timing are positively associated with it. Furthermore, FOI is also estimated to be greater in regions with a larger fraction of older adult people, who are at higher risk to develop severe infections. Our results highlight that temperature plays a key role in shaping WNV outbreak magnitude in Serbia, confirming the association between spring climatic conditions and WNV human transmission risk and thus pointing out the importance of this factor as a potential early warning predictor for timely application of preventive and control measures.

Salmonella Typhimurium caused an unprecedentedly large foodborne outbreak in Finland in 2021.

Salmonella infections are significant causes of foodborne outbreaks in the European Union. This study investigates a sudden increase in gastroenteritis patients in the hospital district of Central Finland in June 2021. The primary aim was to study the outbreak's magnitude and source of the outbreak. Epidemiological, microbiological, environmental and traceback investigations were conducted. Over 700 persons fell ill during the outbreak caused by Salmonella Typhimurium associated with a daycare lunch. Similar S. Typhimurium was found in the patients and a vegetable mix containing iceberg lettuce, cucumber and peas served during lunch. The traceback investigation revealed that the batch information of vegetables from the wholesaler was not complete. The wholesaler had received quality complaints about the iceberg lettuce from the central kitchen. The manufacturer did not test the suspected batch for Salmonella since the production plant had given a certificate declaring it Salmonella negative. The most suspect ingredient was one batch of iceberg lettuce due to quality complaints. The lettuce had not been served in two daycare centres without cases. We recommend that in order to enable thorough microbiological investigation, institutional kitchens store the food samples separately as part of the internal quality control and that food items should always be tested when Salmonella contamination in an outbreak is suspected.

Understanding the implications of under-reporting, vaccine efficiency and social behavior on the post-pandemic spread using physics informed neural networks: A case study of China.

In late 2019, the emergence of COVID-19 in Wuhan, China, led to the implementation of stringent measures forming the zero-COVID policy aimed at eliminating transmission. Zero-COVID policy basically aimed at completely eliminating the transmission of COVID-19. However, the relaxation of this policy in late 2022 reportedly resulted in a rapid surge of COVID-19 cases. The aim of this work is to investigate the factors contributing to this outbreak using a new SEIR-type epidemic model with time-dependent level of immunity. Our model incorporates a time-dependent level of immunity considering vaccine doses administered and time-post-vaccination dependent vaccine efficacy. We find that vaccine efficacy plays a significant role in determining the outbreak size and maximum number of daily infected. Additionally, our model considers under-reporting in daily cases and deaths, revealing their combined effects on the outbreak magnitude. We also introduce a novel Physics Informed Neural Networks (PINNs) approach which is extremely useful in estimating critical parameters and helps in evaluating the predictive capability of our model.

Coronavirüs Hastalığının Halk Sağlığı Etkisinin Ölçülmesi 2019: Pandemi Zamanlarında Sağlık Hizmetlerinde Yeni Bir Perspektif

Since the spark of the recent Severe Acute Respiratory Syndrome CoronaVirus 2 (SARS-CoV-2), public
 health concerns have motivated the accumulation of a vast amount of data about the Coronavirus
 Disease 2019 (COVID-19). The most important metrics for the pandemic progression are the recorded
 cases and reported deaths datasets which were comprehensively collected pertaining to the outbreak.
 The reliance on the census of morbidity and mortality lists solely appeared to be inadequate to assess
 or forecast the disease. It is proposed that a significant extension of this data should be amended to be
 much more useful for public health authorities and official organizations. It would be plausible to adopt
 a practical use of quantitative metrics that could be easily understandable and applied for measuring
 such a catastrophic pandemic. Three parameters that might be observed primarily involve assessing
 the outbreak magnitude, rate of change with time and the degree of stability of the difference in the rate
 of morbidities and mortalities at different intervals. In addition, empirical modeling implementation using
 the curve-fitting approach could be conducted to describe the pattern of the epidemic according to the
 cumulative daily datasets

Dengue transmission dynamics prediction by combining metapopulation networks and Kalman filter algorithm.

Predicting the specific magnitude and the temporal peak of the epidemic of individual local outbreaks is critical for infectious disease control. Previous studies have indicated that significant differences in spatial transmission and epidemic magnitude of dengue were influenced by multiple factors, such as mosquito population density, climatic conditions, and population movement patterns. However, there is a lack of studies that combine the above factors to explain their complex nonlinear relationships in dengue transmission and generate accurate predictions. Therefore, to study the complex spatial diffusion of dengue, this research combined the above factors and developed a network model for spatiotemporal transmission prediction of dengue fever using metapopulation networks based on human mobility. For improving the prediction accuracy of the epidemic model, the ensemble adjusted Kalman filter (EAKF), a data assimilation algorithm, was used to iteratively assimilate the observed case data and adjust the model and parameters. Our study demonstrated that the metapopulation network-EAKF system provided accurate predictions for city-level dengue transmission trajectories in retrospective forecasts of 12 cities in Guangdong province, China. Specifically, the system accurately predicts local dengue outbreak magnitude and the temporal peak of the epidemic up to 10 wk in advance. In addition, the system predicted the peak time, peak intensity, and total number of dengue cases more accurately than isolated city-specific forecasts. The general metapopulation assimilation framework presented in our study provides a methodological foundation for establishing an accurate system with finer temporal and spatial resolution for retrospectively forecasting the magnitude and temporal peak of dengue fever outbreaks. These forecasts based on the proposed method can be interoperated to better support intervention decisions and inform the public of potential risks of disease transmission.

Donuts for weight loss? A norovirus outbreak associated with a bakery in the Australian Capital Territory.

An outbreak of gastroenteritis was investigated following complaints of illness after eating donuts from a food premises in the Australian Capital Territory (ACT). Food poisoning complainants and contacts were surveyed using a standard gastroenteritis questionnaire including menu items from the food premises. Descriptive analyses were performed on data collected for all responses. A case-control study was conducted for a group of 140 people at a catered function. Food safety inspections were conducted with food and environmental samples tested at the ACT Government Analytical Laboratory. Stool specimens were collected from cases who were ill at the time of interview. Neither active case finding, nor viral testing of food or environmental samples, could be conducted. Three hundred and one people were surveyed, and 215 individuals (71.4%) reported vomiting and/or diarrhoea following consumption of a donut purchased from the business over a five-day period. All ill respondents reported eating a donut. The medians of incubation period and illness duration were 34 hours (interquartile range, IQR: 29-42 hours) and 48 hours (IQR: 29-72 hours) respectively. Diarrhoea, vomiting and abdominal pain were the most commonly reported symptoms. Eight out of 11 specimens collected from ill individuals were positive for norovirus. For the case-control study, data from 59 attendees were collected, with an attack rate of 46% (27/59). Eating any kind of filled donut was associated with a person becoming ill (odds ratio: 10.4; 95% confidence interval: 1.18-478.13). No single flavour was identified as the likely source of infection. Elevated levels of coliforms were present in two samples of donut filling obtained during the food safety inspection. Donuts are a novel vehicle for norovirus infection. This implicated pathogen, plus evidence collected at the food premises suggestive of faecal contamination, indicates the source of this outbreak was likely an ill food handler. The findings of this outbreak highlight the importance of excluding food handlers from work while ill. While this was one of the largest foodborne outbreaks investigated in the ACT, the true extent of illness remains unknown. Active case finding should be pursued to determine the magnitude of outbreaks.

A new approach to evaluate the risk of bark beetle outbreaks using multi-step machine learning methods

Bark beetles (Coleoptera: Curculionidae) alter forest ecosystem functioning through tree mortality, thus causing billions of dollars in economic and ecological damages worldwide. Dendroctonus frontalis Zimmermann is considered one of the most significant insect pest species of pine (Pinus spp.) trees in the United States (U.S.), Central America, and Mexico. To manage this threat, research has sought to predict and forecast outbreaks and identify high risk areas to focus on preventative management and reduce economic losses. Prior work has focused on using environmental predictors, but limitations have arisen regarding data availability and structure. Our research objective was to improve on current D. frontalis outbreak prediction models using contemporary modeling techniques. Beetle outbreak data were obtained from the United States Department of Agriculture Forest Service (USDA-FS) and were paired with three spatial–temporal beetle outbreak dynamics from the prior year, fifteen climate variables (DAYMET and WORLDCLIM) (temperature, radiation, wind, and water resources), two terrain attributes (NASA) (elevation and compound topographic index), and four vegetation indices [Moderate Resolution Imaging Spectroradiometer (MODIS)] [maximum and mean normalized difference vegetation index (NDVI)] as predictive features. Extreme gradient boosting was used to create two separate models that predicted the probability and magnitude of beetle outbreaks, which were used to create interpolated prediction maps for the southeastern U.S. The interpolated maps were combined to estimate outbreak risk (i.e., risk = probability of outbreak × magnitude of outbreak). Overall model accuracy was 87.7% when tested on an independent dataset. Distance to prior year outbreak and mean NDVI were the most important features when predicting the probability of outbreak, while summer maximum temperature, distance to prior year outbreak, and winter minimum temperature were the highest weighted features when predicting the magnitude. Results indicated that most of the southeastern U.S. was at low risk (<0.0001% damage per hectare) for the years 2008–2020. Risk was highest for 2012, 2016, and 2017. A few areas in Alabama, Georgia, northern Florida, and South Carolina contained stands at higher risk for damage (>0.01% per hectare) and some locations were at risk for >90% damage per hectare. Extreme gradient boosting paired with outbreak probability and magnitude performed well and is proposed as a solution for future bark beetle prediction and forecasting for more timely management strategies. The inclusion of climatic variables in outbreak models allows for forecasting the effects of future climate change on pine pest populations globally.

The distinguishing NS5-M114V mutation in American Zika virus isolates has negligible impacts on virus replication and transmission potential.

During 2015–2016, outbreaks of Zika virus (ZIKV) occurred in Southeast Asia and the Americas. Most ZIKV infections in humans are asymptomatic, while clinical manifestation is usually a self-limiting febrile disease with maculopapular rash. However, ZIKV is capable of inducing a range of severe neurological complications collectively described as congenital Zika syndrome (CZS). Notably, the scale and magnitude of outbreaks in Southeast Asia were significantly smaller compared to those in the Americas. Sequence comparison between epidemic-associated ZIKV strains from Southeast Asia with those from the Americas revealed a methionine to valine substitution at residue position 114 of the NS5 protein (NS5-M114V) in all the American isolates. Using an American isolate of ZIKV (Natal), we investigated the impact of NS5-M114V mutation on virus replication in cells, virulence in interferon (IFN) α/β receptor knockout (Ifnar-/-) mice, as well as replication and transmission potential in Aedes aegypti mosquitoes. We demonstrated that NS5-M114V mutation had insignificant effect on ZIKV replication efficiency in cells, its ability to degrade STAT2, and virulence in vivo, albeit viremia was slightly prolonged in mice. Furthermore, NS5-M114V mutation decreased mosquito infection and dissemination rates but had no effect on virus secretion into the saliva. Taken together, our findings support the notion that NS5-M114V mutation is unlikely to be a major determinant for virus replication and transmission potential.

Social distancing practice and associated factors in response to COVID-19 pandemic at West Guji Zone, Southern Ethiopia, 2021: A community based cross-sectional study.

Curtailing physical contact between individuals reduces transmission and spread of the disease. Social distancing is an accepted and effective strategy to delay the disease spread and reduce the magnitude of outbreaks of pandemic COVID-19. However, no study quantified social distancing practice and associated factors in the current study area. Therefore, the study aimed to assess social distancing practice and associated factors in response to COVID-19 pandemic in West Guji Zone, Southern Ethiopia, 2020. A Community based cross-sectional study design was conducted among randomly selected 410 household members of Bule Hora Town, West Guji Zone. Data were collected by pre-tested interviewer administered structured questionnaire adapted from previous peer reviewed articles. The data were coded and entered in to Epi data version 3.5 and analyzed by SPSS version 23. The bivariate and multivariate logistic regressions analysis was done to identify factors associated with social distancing practice. Adjusted odds ratio with 95% confidence interval and p value <0.05 were used to declare statistical significance. Out of 447 planned samples, 410 participants were successfully interviewed and included into final analysis; making the response rate of 91.7%. The median (±IQR) age of study participants was 28(±9) years. In this study, 38.3% [95% CI: 33.5%, 43.1%)] of the study participants have good social distancing practices for the prevention of COVID-19. Age group 26-30 years [AOR = 2.56(95% CI: 1.18-5.54)] and 31-35 years [AOR = 3.57(95%CI: 1.56-8.18)], employed [AOR = 6.10(95%CI: 3.46-10.74)],poor knowledge [AOR = 0.59 (95% CI:0.36-0.95)], negative attitude [AOR = 0.55 (95% CI:0.31-0.95)] and low perceived susceptibility [AOR = 0.33(95%CI: 0.20-0.54)] were significantly associated with good social distancing practice. Social distancing practice is relatively poor in the study area. The knowledge and attitude level of participants were identified to be the major factors for the observed poor social distancing practice. Sustained efforts to improve awareness and attitudes towards COVID-19 prevention might improve adherence to social distancing practices.

Investigating the association between mass transit adoption and COVID-19 infections in US metropolitan areas

Urbanization introduces the threat of increased epidemic disease transmission resulting from crowding on mass transit. The coronavirus disease 2019 (COVID-19) pandemic, which has directly led to over 600,000 deaths in the US as of July 2021, triggered mass social distancing policies to be enacted as a key deterrent of widespread infections. Social distancing can be challenging in confined spaces required for transportation such as mass transit systems. Little is published regarding the degree to which mass transit system adoption effects impacted the rise of the COVID-19 pandemic in urban centers. Taking an ecological approach where areal data are the unit of observation, this national-scale study aims to measure the association between the adoption of mass transit and COVID-19 spread through confirmed cases in US metropolitan areas. National survey-based transit adoption measures are entered in negative binomial regression models to evaluate differences between areas. The model results demonstrate that mass transit adoption in US metropolitan areas was associated with the magnitude of outbreaks. Higher incidence of COVID-19 early in the pandemic was associated with survey results conveying higher transit use. Increasing weekly bus transit usage in metropolitan statistical areas by one scaled unit was associated with a 1.38 [95% CI: (1.25, 1.90)] times increase in incidence rate of COVID-19; a one scaled unit increase in weekly train transit usage was associated with an increase in incidence rate of 1.54 [95% CI: (1.42, 2.07)] times. These conclusions should inform early action practices in urban centers with busy transit systems in the event of future infectious disease outbreaks. Deeper understanding of these observed associations may also benefit modeling efforts by allowing researchers to include mathematical adjustments or better explain caveats to results when communicating with decision makers and the public in the crucial early stages of an epidemic.

Predicted Impact of the Lockdown Measure in Response to Coronavirus Disease 2019 (COVID-19) in Greater Bangkok, Thailand, 2021.

In mid-2021, Thailand faced a fourth wave of Coronavirus Disease 2019 (COVID-19) predominantly fueled by the Delta and Alpha variants. The number of cases and deaths rose exponentially, alongside a sharp increase in hospitalizations and intubated patients. The Thai Government then implemented a lockdown to mitigate the outbreak magnitude and prevent cases from overwhelming the healthcare system. This study aimed to model the severity of the outbreak over the following months by different levels of lockdown effectiveness. Secondary analysis was performed on data primarily obtained from the Ministry of Health; the data were analyzed using both the deterministic compartmental model and the system dynamics model. The model was calibrated against the number of daily cases in Greater Bangkok during June–July 2021. We then assessed the outcomes (daily cases, daily deaths, and intubated patients) according to hypothetical lockdowns of varying effectiveness and duration. The findings revealed that lockdown measures could reduce and delay the peak of COVID-19 cases and deaths. A two-month lockdown with 60% effectiveness in the reduction in reproduction number caused the lowest number of cases, deaths, and intubated patients, with a peak about one-fifth of the size of a no-lockdown peak. The two-month lockdown policy also delayed the peak until after December, while in the context of a one-month lockdown, cases peaked during the end of September to early December (depending on the varying degrees of lockdown effectiveness in the reduction in reproduction number). In other words, the implementation of a lockdown policy did not mean the end of the outbreak, but it helped delay the peak. In this sense, implementing a lockdown helped to buy time for the healthcare system to recover and better prepare for any future outbreaks. We recommend further studies that explore the impact of lockdown measures at a sub-provincial level, and examine the impact of lockdowns on parameters not directly related to the spread of disease, such as quality of life and economic implications for individuals and society.

Contributing to a Healthier Nigeria through Public Health Workforce Capacity Development: Afenet Experience in Nigeria

The recent upsurge in the number and magnitude of outbreaks of infectious diseases in Nigeria has been managed effectively and efficiently by the Nigeria Centre for Disease Control (NCDC) in an increasingly epidemiology guided approach led by a skilled workforce within the one health collaborative agenda.1–4 The increasing number of the outbreaks of re-emerging infectious diseases could not have been unconnected to the effect of global warming with its resultant effect in the changing population activities and interaction with nature.5–7 Nigeria could not have effectively contained these emerging public health challenges had she not taken steps in developing her public health infrastructure, the laboratory capabilities and manpower development. Since 2008, the Nigeria Field Epidemiology Laboratory Training Program (NFELTP), a collaborative effort by NCDC, Federal Ministry of Health, Federal Ministry of Agriculture and Rural Development, African Field Epidemiology Network (AFENET) with financial and technical support from the US Centers for Disease Control and Prevention (CDC) has been building epidemiology capacity for disease prevention and control. Field epidemiology, the art and practice of epidemiology in the field, has been adopted as an effective tool and practice in preventing, detecting and controlling emerging public health threats and crisis. Field epidemiology provides the scientific evidence for public health actions.

Target product profile for a dengue pre-vaccination screening test.

With increasing geographic spread, frequency, and magnitude of outbreaks, dengue continues to pose a major public health threat worldwide. Dengvaxia, a dengue live-attenuated tetravalent vaccine, was licensed in 2015, but post hoc analyses of long-term data showed serostatus-dependent vaccine performance with an excess risk of hospitalized and severe dengue in seronegative vaccine recipients. The World Health Organization (WHO) recommended that only persons with evidence of past dengue infection should receive the vaccine. A test for pre-vaccination screening for dengue serostatus is needed. To develop the target product profile (TPP) for a dengue pre-vaccination screening test, face-to-face consultative meetings were organized with follow-up regional consultations. A technical working group was formed to develop consensus on a reference test against which candidate pre-vaccination screening tests could be compared. The group also reviewed current diagnostic landscape and the need to accelerate the evaluation, regulatory approval, and policy development of tests that can identify seropositive individuals and maximize public health impact of vaccination while avoiding the risk of hospitalization in dengue-naive individuals. Pre-vaccination screening strategies will benefit from rapid diagnostic tests (RDTs) that are affordable, sensitive, and specific and can be used at the point of care (POC). The TPP described the minimum and ideal characteristics of a dengue pre-vaccination screening RDT with an emphasis on high specificity. The group also made suggestions for accelerating access to these RDTs through streamlining regulatory approval and policy development. Risk and benefit based on what can be achieved with RDTs meeting minimal and optimal characteristics in the TPP across a range of seroprevalences were defined. The final choice of RDTs in each country will depend on the performance of the RDT, dengue seroprevalence in the target population, tolerance of risk, and cost-effectiveness.

Trends in Outbreaks of Defoliating Insects Highlight Growing Threats for Central European Forests, and Implications for Eastern Baltic Region

To identify general patterns in the effect of climate-driven changes in the outbreak frequency of forest defoliating species, we examined 60 years of records (1950–2010) of outbreaks of five defoliating species. Data on Lymantria dispar, Lymantria monacha, Bupalus piniarius, Panolis flammea, and Operophtera brumata from five Central European countries (Slovakia, Czech Republic, Austria, Hungary, and Germany), where the current climate is comparable with the projections of climate for the Eastern Baltic region by the end of the 21st century, were analyzed. Time series approach was applied to estimate the linkage between outbreaks and climate warming. Mean annual, summer, and winter deviations for the period of 1850 to 1900 were assessed as proxies of warming. To estimate the legacy effect, warming proxies were lagged by one year. Among those tested, warming proxies showed a linkage with outbreaks. Three significant outbreaks occurred in the analyzed period (at the beginning and end of the period). During the middle part of the analyzed period, the frequency and magnitude of outbreaks were low, implicating a higher insect outbreak risk with warming in Central Europe. In the latter part of the analyzed period, more frequent yet smaller outbreaks occurred, which supports the outbreak linkage with one-year lag, summer, and annual temperatures.

Using heterogeneous data to identify signatures of dengue outbreaks at fine spatio-temporal scales across Brazil.

Dengue virus remains a significant public health challenge in Brazil, and seasonal preparation efforts are hindered by variable intra- and interseasonal dynamics. Here, we present a framework for characterizing weekly dengue activity at the Brazilian mesoregion level from 2010–2016 as time series properties that are relevant to forecasting efforts, focusing on outbreak shape, seasonal timing, and pairwise correlations in magnitude and onset. In addition, we use a combination of 18 satellite remote sensing imagery, weather, clinical, mobility, and census data streams and regression methods to identify a parsimonious set of covariates that explain each time series property. The models explained 54% of the variation in outbreak shape, 38% of seasonal onset, 34% of pairwise correlation in outbreak timing, and 11% of pairwise correlation in outbreak magnitude. Regions that have experienced longer periods of drought sensitivity, as captured by the “normalized burn ratio,” experienced less intense outbreaks, while regions with regular fluctuations in relative humidity had less regular seasonal outbreaks. Both the pairwise correlations in outbreak timing and outbreak trend between mesoresgions were best predicted by distance. Our analysis also revealed the presence of distinct geographic clusters where dengue properties tend to be spatially correlated. Forecasting models aimed at predicting the dynamics of dengue activity need to identify the most salient variables capable of contributing to accurate predictions. Our findings show that successful models may need to leverage distinct variables in different locations and be catered to a specific task, such as predicting outbreak magnitude or timing characteristics, to be useful. This advocates in favor of “adaptive models” rather than “one-size-fits-all” models. The results of this study can be applied to improving spatial hierarchical or target-focused forecasting models of dengue activity across Brazil.

Simulation-based estimation of the early spread of COVID-19 in Iran: actual versus confirmed cases.

Understanding the state of the COVID‐19 pandemic relies on infection and mortality data. Yet official data may underestimate the actual cases due to limited symptoms and testing capacity. We offer a simulation‐based approach which combines various sources of data to estimate the magnitude of outbreak. Early in the epidemic we applied the method to Iran's case, an epicenter of the pandemic in winter 2020. Estimates using data up to March 20th, 2020, point to 916,000 (90% UI: 508 K, 1.5 M) cumulative cases and 15,485 (90% UI: 8.4 K, 25.8 K) total deaths, numbers an order of magnitude higher than official statistics. Our projections suggest that absent strong sustaining of contact reductions the epidemic may resurface. We also use data and studies from the succeeding months to reflect on the quality of original estimates. Our proposed approach can be used for similar cases elsewhere to provide a more accurate, early, estimate of outbreak state.© 2020 System Dynamics Society

Assessment of the efficacy of routine vaccination on the magnitude of Foot and Mouth Disease outbreak in Kafrelsheikh governorate, Delta Region, Egypt

Foot and mouth disease (FMD) is a highly contagious viral disease causes a serious economic impact on livestock production and trading. FMD is an endemic disease in Egypt and a national control program that depends on routine obligatory vaccination of all ruminant species is being followed for disease control. A nation-wide epidemic of FMD was commenced in early 2015 and typical clinical signs of the disease were observed even in vaccinated animals. The morbidity and case fatality rates were high enough to be investigated. In the current study, non-vaccinated and vaccinated animals of different sex and ages were examined to evaluate the efficacy of FMD different vaccines used in Egypt. Clinical, post-mortem and serological examinations were used to confirm the infection, while the molecular investigation was applied to identify the serotype responsible for this epidemic. The incidence rate and the attributable proportion (fraction) of FMD cases which could be avoided by vaccination and vaccine efficacy were calculated. The obtained results confirmed the infection with FMD virus (FMDV) serotype O in both non-vaccinated and vaccinated animals. The incidence of FMD was 86.67% among non-vaccinated animals, while it was ranged from 15% to 31.8% among vaccinated animals according to the type of vaccine used. The attributable fraction was 73.9% and the efficacy of the three used vaccines was 63.3%, 76.92% and 82.25% for Tri-Aphthovac, VSVRI and Meriel vaccines, respectively. In conclusion, vaccination in Egypt is able to minimize the magnitude of outbreaks caused by the same serotype found in the vaccine but was not able to prevent the infection and eliminate the disease. The highest vaccination efficacy was found in Mid-aged animals and male cattle.

Inter-outbreak stability reflects the size of the susceptible pool and forecasts magnitudes of seasonal epidemics

For dengue fever and other seasonal epidemics we show how the stability of the preceding inter-outbreak period can predict subsequent total outbreak magnitude, and that a feasible stability metric can be computed from incidence data alone. As an observable of a dynamical system, incidence data contains information about the underlying mechanisms: climatic drivers, changing serotype pools, the ecology of the vector populations, and evolving viral strains. We present mathematical arguments to suggest a connection between stability measured in incidence data during the inter-outbreak period and the size of the effective susceptible population. The method is illustrated with an analysis of dengue incidence in San Juan, Puerto Rico, where forecasts can be made as early as three to four months ahead of an outbreak. These results have immediate significance for public health planning, and can be used in combination with existing forecasting methods and more comprehensive dengue models.

Droughts drive outbreak dynamics of an invasive forest insect on an exotic host

Insect outbreaks are among the most important biotic disturbances in forest ecosystems and can exert immense economic and ecological impacts. Understanding the spatio-temporal patterns of eruptive insects can provide insights into the mechanisms driving their dynamics and help predict future responses under climate change. The aims of this study were to analyze the spatio-temporal patterns of outbreaks of the woodwasp Sirex noctilio –a major invasive pest of pines– in an invaded region of South America, to assess the relative importance of density-dependent and density-independent mechanisms on population dynamics, and to identify the primary factors that influence the magnitude of outbreaks. We used tree ring and insect sampling data of more than 1000 trees to reconstruct S. noctilio outbreaks in 29 pine stands across Patagonia-Argentina over a 16-year period. We found marked spatial synchrony in S. noctilio outbreaks at a regional scale. Rates of tree mortality from S. noctilio were influenced by both density-dependent and density-independent factors. The occurrence of S. noctilio outbreaks are triggered by an abrupt increase in drought, which likely increases the availability of susceptible host trees. The damage caused by the outbreaks is determined by stand level variables, closely related with tree stress. Rates of tree mortality decreased over time due to negative density-dependence in S. noctilio populations, likely due to the loss of suitable resources over time. Depicting mechanisms of large-scale tree mortality in ecosystems provides insights to the drivers of forest outbreaks and other factors such as responses to a changing climate.

Risk prediction system for dengue transmission based on high resolution weather data.

BackgroundDengue is the fastest spreading vector-borne viral disease, resulting in an estimated 390 million infections annually. Precise prediction of many attributes related to dengue is still a challenge due to the complex dynamics of the disease. Important attributes to predict include: the risk of and risk factors for an infection; infection severity; and the timing and magnitude of outbreaks. In this work, we build a model for predicting the risk of dengue transmission using high-resolution weather data. The level of dengue transmission risk depends on the vector density, hence we predict risk via vector prediction.Methods and findingsWe make use of surveillance data on Aedes aegypti larvae collected by the Taiwan Centers for Disease Control as part of the national routine entomological surveillance of dengue, and weather data simulated using the IBM’s Containerized Forecasting Workflow, a high spatial- and temporal-resolution forecasting system. We propose a two stage risk prediction system for assessing dengue transmission via Aedes aegypti mosquitoes. In stage one, we perform a logistic regression to determine whether larvae are present or absent at the locations of interest using weather attributes as the explanatory variables. The results are then aggregated to an administrative division, with presence in the division determined by a threshold percentage of larvae positive locations resulting from a bootstrap approach. In stage two, larvae counts are estimated for the predicted larvae positive divisions from stage one, using a zero-inflated negative binomial model. This model identifies the larvae positive locations with 71% accuracy and predicts the larvae numbers producing a coverage probability of 98% over 95% nominal prediction intervals. This two-stage model improves the overall accuracy of identifying larvae positive locations by 29%, and the mean squared error of predicted larvae numbers by 9.6%, against a single-stage approach which uses a zero-inflated binomial regression approach.ConclusionsWe demonstrate a risk prediction system using high resolution weather data can provide valuable insight to the distribution of risk over a geographical region. The work also shows that a two-stage approach is beneficial in predicting risk in non-homogeneous regions, where the risk is localised.