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Non-pharmaceutical personal protective (NPP) measures such as face masks use, and hand and respiratory hygiene can be effective measures for mitigating the spread of aerosol/airborne diseases, such as COVID-19, in the absence of vaccination or treatment. However, the usage of such measures is constrained by their inherent perceived cost and effectiveness for reducing transmission risk. To understand the complex interaction of disease dynamics and individuals decision whether to adopt NPP or not, we incorporate evolutionary game theory into an epidemic model such as COVID-19. To compare how self-interested NPP use differs from social optimum, we also investigated optional control from a central planner's perspective. We use Pontryagin's maximum principle to identify the population-level NPP uptake that minimizes disease incidence by incurring the minimum costs. The evolutionary behavior model shows that NPP uptake increases at lower perceived costs of NPP, higher transmission risk, shorter duration of NPP use, higher effectiveness of NPP, and shorter duration of disease-induced immunity. Though social optimum NPP usage is generally more effective in reducing disease incidence than self-interested usage, our analysis identifies conditions under which both strategies get closer. Our model provides new insights for public health in mitigating a disease outbreak through NPP., Competing Interests: Declaration of competing interest The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Influenza in commercial swine populations leads to reduced gain in fattening pigs and reproductive issues in sows. This literature review aims to analyze the contributions of mathematical modeling in understanding influenza transmission and control among domestic swine. Twenty-two full-text research articles from seven databases were reviewed, categorized into swine-only ( n = 13), swine-avian ( n = 3), and swine-human models ( n = 6). Strains of influenza models were limited to H1N1 ( n = 7) and H3N2 ( n = 1), with many studies generalizing the disease as influenza A. Half of the studies ( n = 14) considered at least one control strategy, with vaccination being the primary investigated strategy. Vaccination was shown to reduce disease prevalence in single animal cohorts. With a continuous flow of new susceptible animals, such as in farrow-to-finish farms, it was shown that influenza became endemic despite vaccination strategies such as mass or batch-to-batch vaccination. Human vaccination was shown to be effective at mitigating human-to-human influenza transmission and to reduce spillover events from pigs. Current control strategies cannot stop influenza in livestock or prevent viral reassortment in swine, so mechanistic models are crucial for developing and testing new biosecurity measures to prevent future swine pandemics.

Background: Dengue fever is an arboviral disease caused by the dengue virus (DENV). Its geographical distribution and health burden have been steadily increasing through tropical and subtropical climates in recent decades., Methods: We developed a temperature- and precipitation-dependent mechanistic model for the global risk of dengue fever outbreaks using the basic reproduction number (R 0 ) as the metric of disease transmission risk. We used our model to evaluate the global risk of dengue outbreaks from 1950 to 2020 and to investigate the impact of annual seasons and El Niño events., Results: We showed that the global annual risk of dengue outbreaks has steadily increased during the last four decades. Highest R 0 values were observed in South America, Southeast Asia, and the Equatorial region of Africa year-round with large seasonal variations occurring in other regions. El Niño was shown to be positively correlated with the global risk of dengue outbreaks with a correlation of 0.52. However, the impact of El Niño on dengue R 0 was shown to vary across geographical regions and between El Niño events., Conclusions: Strong El Niño events may increase the risk of dengue outbreaks across the globe. The onset of these events may trigger a surge of control efforts to minimize risk of dengue outbreaks., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Diseases in the swine industry can cause significant economic and health impacts. This review examines R 0 estimates for respiratory diseases in pigs, assessing variations and comparing transmission risks within and between farms. A literature search of three databases aggregated peer-reviewed research articles on swine viral respiratory diseases' R 0 values. The study focused on seven diseases: Aujeszky's disease (AD), Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Circovirus, Influenza A (IA), Encephalomyocarditis Virus (EV), Classical Swine Fever (CSF), and African Swine Fever (ASF). R 0 values were estimated for transmission within and between herds/farms using various methods, from complex mathematical models to simple calculations. Data primarily came from disease surveillance and laboratory experiments. The median R 0 for within-herd and between-herd transmission was 10 and 3.31 for AD, 2.78 and 1.14 for PRRSV, 5.9 and 0.89 for Circovirus, 1.75 and 1.6 for CSF, and 3.94 and 3.15 for ASF. For IA and EV, only within-herd R 0 values were estimated at 8.65 and 1.3, respectively. Diseases with high R 0 values highlight the need for prompt detection and response to outbreaks. Continuous monitoring and evaluation of pathogen transmissibility are crucial for enhancing disease surveillance and reducing the impact of livestock diseases.

Background: The conventional diagnostic for Schistosoma mansoni infection is stool microscopy with the Kato-Katz (KK) technique to detect eggs. Its outcomes are highly variable on a day-to-day basis and may lead to biased estimates of community infection used to inform public health programs. Our goal is to develop a resampling method that leverages data from a large-scale randomized trial to accurately predict community infection., Methods: We developed a resampling method that provides unbiased community estimates of prevalence, intensity and other statistics for S mansoni infection when a community survey is conducted using KK stool microscopy with a single sample per host. It leverages a large-scale data set, collected in the Schistosomiasis Consortium for Operational Research and Evaluation (SCORE) project, and allows linking single-stool specimen community screening to its putative multiday "true statistics.", Results: SCORE data analysis reveals the limited sensitivity of KK stool microscopy and systematic bias of single-day community testing versus multiday testing; for prevalence estimate, it can fall up to 50% below the true value. The proposed SCORE cluster method reduces systematic bias and brings the estimated prevalence values within 5%-10% of the true value. This holds for a broad swath of transmission settings, including SCORE communities, and other data sets., Conclusions: Our SCORE cluster method can markedly improve the S mansoni prevalence estimate in settings using stool microscopy., Competing Interests: Potential conflicts of interest. All authors: No reported conflicts., (© The Author(s) 2023. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Objective: The MGDrivE (MGDrivE 1 and MGDrivE 2) modeling framework provides a flexible and expansive environment for testing the efficacy of novel gene-drive constructs for the control of mosquito-borne diseases. However, the existing model framework did not previously support several features necessary to simulate some types of intervention strategies. Namely, current MGDrivE versions do not permit modeling of small molecule inducible systems for controlling gene expression in gene drive designs or the inheritance patterns of self-eliminating gene drive mechanisms., Results: Here, we demonstrate a new MGDrivE 2 module that permits the simulation of gene drive strategies incorporating small molecule-inducible systems and self-eliminating gene drive mechanisms. Additionally, we also implemented novel sparsity-aware sampling algorithms for improved computational efficiency in MGDrivE 2 and supplied an analysis and plotting function applicable to the outputs of MGDrivE 1 and MGDrivE 2., (© 2023. BioMed Central Ltd., part of Springer Nature.)

Chagas disease, caused by Trypanosoma cruzi and transmitted by triatomines, can lead to severe cardiac issues and mortality in many mammals. Recent studies have shown that systemic insecticide treatment of dogs is highly effective in killing triatomines. Here, we assessed the impact of dog treatment on T. cruzi transmission. We developed a mathematical model of T. cruzi transmission among triatomines, dogs, humans, and rodents. We used the model to evaluate the impact of dog treatment regimens on T. cruzi transmission dynamics to determine their effectiveness in reducing T. cruzi infection among hosts. We show that a 3-month treatment regimen may reduce T. cruzi incidence among humans by 59-80% in a high transmission setting, and 26-82% in a low transmission setting. An annual treatment may reduce incidence among humans by 49-74% in a high transmission setting, and by 11-76% in a low transmission setting. However, dog treatment may substantially increase T. cruzi prevalence among dogs if dog consumption of dead triatomines increases. Our model indicates that dog treatment may reduce T. cruzi infections among humans, but it may increase infections in dogs. Therefore, a holistic approach targeting different hosts is necessary for Chagas elimination.

The US Centers for Disease Control and Prevention (CDC) defines a county metric of coronavirus disease 2019 (COVID-19) community levels to inform public health measures. We find that the COVID-19 community levels vary frequently over time, which may not be optimal for decision making. Alternative metric formulations that do not compromise predictive ability are shown to reduce variability., (© The Author(s) 2022. Published by Oxford University Press on behalf of Infectious Diseases Society of America.)

Chagas disease is a zoonotic vector-borne disease caused by the parasite Trypanosoma cruzi , which affects a variety of mammalian species across the Americas, including humans and dogs. Mathematical modeling has been widely used to investigate the transmission dynamics and control of vector-borne diseases. We performed a scoping review of mathematical models that investigated the role of dogs in T. cruzi transmission. We identified ten peer-reviewed papers that have explicitly modeled the role of dogs in Chagas transmission dynamics. We discuss the different methods employed in these studies, the different transmission metrics, disease transmission routes, and disease control strategies that have been considered and evaluated. In general, mathematical modeling studies have shown that dogs are not only at high risk of T. cruzi infection but are also major contributors to T. cruzi transmission to humans. Moreover, eliminating infected dogs from households or frequent use of insecticide was shown to be effective for curtailing T. cruzi transmission in both humans and dogs. However, when insecticide spraying is discontinued, T. cruzi infections in dogs were shown to return to their pre-spraying levels. We discuss the challenges and opportunities for future modeling studies to improve our understanding of Chagas disease transmission dynamics and control.

Background: Canine Chagas disease is caused by the protozoan parasite Trypanosoma cruzi and transmitted by insect triatomine vectors known as kissing bugs. The agent can cause cardiac damage and long-term heart disease and death in humans, dogs, and other mammals. In laboratory settings, treatment of dogs with systemic insecticides has been shown to be highly efficacious at killing triatomines that feed on treated dogs., Method: We developed compartmental vector-host models of T. cruzi transmission between the triatomine and dog population accounting for the impact of seasonality and triatomine migration on disease transmission dynamics. We considered a single vector-host model without seasonality, and model with seasonality, and a spatially coupled model. We used the models to evaluate the effectiveness of the insecticide fluralaner with different durations of treatment regimens for reducing T. cruzi infection in different transmission settings., Results: In low and medium transmission settings, our model showed a marginal difference between the 3-month and 6-month regimens for reducing T. cruzi infection among dogs. The difference increases in the presence of seasonality and triatomine migration from a sylvatic transmission setting. In high transmission settings, the 3-month regimen was substantially more effective in reducing T. cruzi infections in dogs than the other regimens. Our model showed that increased migration rate reduces fluralaner effectiveness in all treatment regimens, but the relative reduction in effectiveness is minimal during the first years of treatment. However, if an additional 10% or more of triatomines killed by dog treatment were eaten by dogs, treatment could increase T. cruzi infections in the dog population at least during the first year of treatment., Conclusion: Our analysis shows that treating all peridomestic dogs every three to six months for at least five years could be an effective measure to reduce T. cruzi infections in dogs and triatomines in peridomestic transmission settings. However, further studies at the local scale are needed to better understand the potential impact of routine use of fluralaner treatment on increasing dogs' consumption of dead triatomines., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2023 Fiatsonu et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Many organizations, including the US Centers for Disease Control and Prevention, have developed risk indexes to help determine community transmission levels for the ongoing COVID-19 pandemic. These risk indexes are largely based on newly reported cases and percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests, which are well-established as biased estimates of COVID-19 transmission. However, transmission risk indexes should accurately and precisely communicate community risks to decision-makers and the public. Therefore, transmission risk indexes would ideally quantify actual, and not just reported, levels of disease prevalence or incidence. Here, we develop a robust data-driven framework for determining and communicating community transmission risk levels using reported cases and test positivity. We use this framework to evaluate the previous CDC community risk level metrics that were proposed as guidelines for determining COVID-19 transmission risk at community level in the US. Using two recently developed data-driven models for COVID-19 transmission in the US to compute community-level prevalence, we show that there is substantial overlap of prevalence between the different community risk levels from the previous CDC guidelines. Using our proposed framework, we redefined the risk levels and their threshold values. We show that these threshold values would have substantially reduced the overlaps of underlying community prevalence between counties/states in different community risk levels between 3/19/2020-9/9/2021. Our study demonstrates how the previous CDC community risk level indexes could have been calibrated to infection prevalence to improve their power to accurately determine levels of COVID-19 transmission in local communities across the US. This method can be used to inform the design of future COVID-19 transmission risk indexes., Competing Interests: Declaration of Competing Interest The authors declare no conflict of interest., (Copyright © 2022 The Authors. Published by Elsevier B.V. All rights reserved.)

Accurate estimates of infection prevalence and seroprevalence are essential for evaluating and informing public health responses and vaccination coverage needed to address the ongoing spread of COVID-19 in each United States (U.S.) state. However, reliable, timely data based on representative population sampling are unavailable, and reported case and test positivity rates are highly biased. A simple data-driven Bayesian semi-empirical modeling framework was developed and used to evaluate state-level prevalence and seroprevalence of COVID-19 using daily reported cases and test positivity ratios. The model was calibrated to and validated using published state-wide seroprevalence data, and further compared against two independent data-driven mathematical models. The prevalence of undiagnosed COVID-19 infections is found to be well-approximated by a geometrically weighted average of the positivity rate and the reported case rate. Our model accurately fits state-level seroprevalence data from across the U.S. Prevalence estimates of our semi-empirical model compare favorably to those from two data-driven epidemiological models. As of December 31, 2020, we estimate nation-wide a prevalence of 1.4% [Credible Interval (CrI): 1.0%-1.9%] and a seroprevalence of 13.2% [CrI: 12.3%-14.2%], with state-level prevalence ranging from 0.2% [CrI: 0.1%-0.3%] in Hawaii to 2.8% [CrI: 1.8%-4.1%] in Tennessee, and seroprevalence from 1.5% [CrI: 1.2%-2.0%] in Vermont to 23% [CrI: 20%-28%] in New York. Cumulatively, reported cases correspond to only one third of actual infections. The use of this simple and easy-to-communicate approach to estimating COVID-19 prevalence and seroprevalence will improve the ability to make public health decisions that effectively respond to the ongoing COVID-19 pandemic., Competing Interests: The authors have declared that no competing interests exist.

Although acute respiratory infections are a leading cause of mortality in sub-Saharan Africa, surveillance of diseases such as influenza is mostly neglected. Evaluating the usefulness of influenza-like illness (ILI) surveillance systems and developing approaches for forecasting future trends is important for pandemic preparedness. We applied and compared a range of robust statistical and machine learning models including random forest (RF) regression, support vector machines (SVM) regression, multivariable linear regression and ARIMA models to forecast 2012 to 2018 trends of reported ILI cases in Cameroon, using Google searches for influenza symptoms, treatments, natural or traditional remedies as well as, infectious diseases with a high burden (i.e., AIDS, malaria, tuberculosis). The R 2 and RMSE (Root Mean Squared Error) were statistically similar across most of the methods, however, RF and SVM had the highest average R 2 (0.78 and 0.88, respectively) for predicting ILI per 100,000 persons at the country level. This study demonstrates the need for developing contextualized approaches when using digital data for disease surveillance and the usefulness of search data for monitoring ILI in sub-Saharan African countries.

Background: Seasonal influenza remains a major cause of morbidity and mortality in the USA. Despite the US Centers for Disease Control and Prevention recommendation promoting the early antiviral treatment of high-risk patients, treatment coverage remains low., Methods: To evaluate the population-level impact of increasing antiviral treatment timeliness and coverage among high-risk patients in the USA, we developed an influenza transmission model that incorporates data on infectious viral load, social contact, and healthcare-seeking behavior. We modeled the reduction in transmissibility in treated individuals based on their reduced daily viral load. The reduction in hospitalizations following treatment was based on estimates from clinical trials. We calibrated the model to weekly influenza data from Texas, California, Connecticut, and Virginia between 2014 and 2019. We considered in the baseline scenario that 2.7-4.8% are treated within 48 h of symptom onset while an additional 7.3-12.8% are treated after 48 h of symptom onset. We evaluated the impact of improving the timeliness and uptake of antiviral treatment on influenza cases and hospitalizations., Results: Model projections suggest that treating high-risk individuals as early as 48 h after symptom onset while maintaining the current treatment coverage level would avert 2.9-4.5% of all symptomatic cases and 5.5-7.1% of all hospitalizations. Geographic variability in the effectiveness of earlier treatment arises primarily from variabilities in vaccination coverage and population demographics. Regardless of these variabilities, we found that when 20% of the high-risk individuals were treated within 48 h, the reduction in hospitalizations doubled. We found that treatment of the elderly population (> 65 years old) had the highest impact on reducing hospitalizations, whereas treating high-risk individuals aged 5-19 years old had the highest impact on reducing transmission. Furthermore, the population-level benefit per treated individual is enhanced under conditions of high vaccination coverage and a low attack rate during an influenza season., Conclusions: Increased timeliness and coverage of antiviral treatment among high-risk patients have the potential to substantially reduce the burden of seasonal influenza in the USA, regardless of influenza vaccination coverage and the severity of the influenza season.

Starting in mid-May 2020, many US states began relaxing social-distancing measures that were put in place to mitigate the spread of COVID-19. To evaluate the impact of relaxation of restrictions on COVID-19 dynamics and control, we developed a transmission dynamic model and calibrated it to US state-level COVID-19 cases and deaths. We used this model to evaluate the impact of social distancing, testing and contact tracing on the COVID-19 epidemic in each state. As of 22 July 2020, we found that only three states were on track to curtail their epidemic curve. Thirty-nine states and the District of Columbia may have to double their testing and/or tracing rates and/or rolling back reopening by 25%, while eight states require an even greater measure of combined testing, tracing and distancing. Increased testing and contact-tracing capacity is paramount for mitigating the recent large-scale increases in US cases and deaths.

Background: Yellow fever (YF) is a vector-borne viral hemorrhagic disease endemic in Africa and Latin America. In 2016, the World Health Organization (WHO) developed the Eliminate YF Epidemics strategy aiming at eliminating YF epidemics by 2026., Methods: We developed a spatiotemporal model of YF, accounting for the impact of temperature, vector distribution, and socioeconomic factors on disease transmission. We validated our model against previous estimates of YF basic reproductive number (R0). We used the model to estimate global risk of YF outbreaks and vaccination efforts needed to achieve elimination of YF epidemics., Results: We showed that the global risk of YF outbreaks is highly heterogeneous. High-risk transmission areas (R0 > 6) are mainly found in West Africa and the Equatorial region of Latin America. We showed that vaccination coverage needed to eliminate YF epidemics in an endemic country varies substantially between districts. In many endemic countries, a 90% vaccination coverage is needed to achieve elimination. However, in some high-risk districts in Africa, a 95% coverage may be required., Conclusions: Global elimination of YF epidemics requires higher population-level immunity than the 80% coverage recommended by the WHO. Optimal YF vaccination strategy should be tailored to the risk profile of each endemic country., (© The Author(s) 2019. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail: journals.permissions@oup.com.)

Mosquito-borne viruses are emerging or re-emerging globally, afflicting millions of people around the world. Aedes aegypti , the yellow fever mosquito, is the principal vector of dengue, Zika, and chikungunya viruses, and has well-established populations across tropical and subtropical urban areas of the Americas, including the southern United States. While intense arboviral epidemics have occurred in Mexico and further south in the Americas, local transmission in the United States has been minimal. Here, we study Ae. aegypti and Culex quinquefasciatus host feeding patterns and vertebrate host communities in residential environments of South Texas to identify host-utilization relative to availability. Only 31% of Ae. aegypti blood meals were derived from humans, while 50% were from dogs and 19% from other wild and domestic animals. In Cx. quinquefasciatus , 67% of blood meals were derived from chicken, 22% came from dogs, 9% from various wild avian species, and 2% from other mammals including one human, one cat, and one pig. We developed a model for the reproductive number, R 0 , for Zika virus (ZIKV) in South Texas relative to northern Mexico using human disease data from Tamaulipas, Mexico. We show that ZIKV R 0 in South Texas communities could be greater than one if the risk of human exposure to Ae. aegypti bites in these communities is at least 60% that of Northern Mexico communities. The high utilization of non-human vertebrates and low risk of human exposure in South Texas diminishes the outbreak potential for human-amplified urban arboviruses transmitted by Ae. aegypti .

Purpose: In 2012, Cameroon experienced a large measles outbreak of over 14,000 cases. To determine the spatio-temporal dynamics of measles transmission in Cameroon, we analyzed weekly case data collected by the Ministry of Health., Methods: We compared several multivariate time-series models of population movement to characterize the spatial spread of measles in Cameroon. Using the best model, we evaluated the contribution of population mobility to disease transmission at increasing geographic resolutions: region, department, and health district., Results: Our spatio-temporal analysis showed that the power law model, which accounts for long-distance population movement, best represents the spatial spread of measles in Cameroon. Population movement between health districts within departments contributed to 7.6% (range: 0.4%-13.4%) of cases at the district level, whereas movement between departments within regions contributed to 16.0% (range: 1.3%-23.2%) of cases. Long-distance movement between regions contributed to 16.7% (range: 0.1%-59.0%) of cases at the region level, 20.1% (range: 7.1%-30.0%) at the department level, and 29.7% (range: 15.3%-47.6%) at the health district level., Conclusions: Population long-distance mobility is an important driver of measles dynamics in Cameroon. These findings demonstrate the need to improve our understanding of the roles of population mobility and local heterogeneity of vaccination coverage in the spread and control of measles in Cameroon., (Copyright © 2019 Elsevier Inc. All rights reserved.)

Since the turn of the century, the global community has made great progress towards the elimination of gambiense human African trypanosomiasis (HAT). Elimination programs, primarily relying on screening and treatment campaigns, have also created a rich database of HAT epidemiology. Mathematical models calibrated with these data can help to fill remaining gaps in our understanding of HAT transmission dynamics, including key operational research questions such as whether integrating vector control with current intervention strategies is needed to achieve HAT elimination. Here we explore, via an ensemble of models and simulation studies, how including or not disease stage data, or using more updated data sets affect model predictions of future control strategies., Competing Interests: The authors have declared that no competing interests exist.

Background: Several modeling studies have been undertaken to assess the feasibility of the WHO goal of eliminating gambiense human African trypanosomiasis (g-HAT) by 2030. However, these studies have generally overlooked the effect of vector migration on disease transmission and control. Here, we evaluated the impact of vector migration on the feasibility of interrupting transmission in different g-HAT foci., Methods: We developed a g-HAT transmission model of a single tsetse population cluster that accounts for migration of tsetse fly into this population. We used a model calibration approach to constrain g-HAT incidence to ranges expected for high, moderate and low transmission settings, respectively. We used the model to evaluate the effectiveness of current intervention measures, including medical intervention through enhanced screening and treatment, and vector control, for interrupting g-HAT transmission in disease foci under each transmission setting., Results: We showed that, in low transmission settings, under enhanced medical intervention alone, at least 70% treatment coverage is needed to interrupt g-HAT transmission within 10 years. In moderate transmission settings, a combination of medical intervention and a vector control measure with a daily tsetse mortality greater than 0.03 is required to achieve interruption of disease transmission within 10 years. In high transmission settings, interruption of disease transmission within 10 years requires a combination of at least 70% medical intervention coverage and at least 0.05 tsetse daily mortality rate from vector control. However, the probability of achieving elimination in high transmission settings decreases with an increased tsetse migration rate., Conclusion: Our results suggest that the WHO 2030 goal of G-HAT elimination is, at least in theory, achievable. But the presence of tsetse migration may reduce the probability of interrupting g-HAT transmission in moderate and high transmission foci. Therefore, optimal vector control programs should incorporate monitoring and controlling of vector density in buffer areas around foci of g-HAT control efforts., Competing Interests: The authors have declared that no competing interests exist.

The interplay between civil unrest and disease transmission is not well understood. Violence targeting healthcare workers and Ebola treatment centers in the Democratic Republic of the Congo (DRC) has been thwarting the case isolation, treatment, and vaccination efforts. The extent to which conflict impedes public health response and contributes to incidence has not previously been evaluated. We construct a timeline of conflict events throughout the course of the epidemic and provide an ethnographic appraisal of the local conditions that preceded and followed conflict events. Informed by temporal incidence and conflict data as well as the ethnographic evidence, we developed a model of Ebola transmission and control to assess the impact of conflict on the epidemic in the eastern DRC from April 30, 2018, to June 23, 2019. We found that both the rapidity of case isolation and the population-level effectiveness of vaccination varied notably as a result of preceding unrest and subsequent impact of conflict events. Furthermore, conflict events were found to reverse an otherwise declining phase of the epidemic trajectory. Our model framework can be extended to other infectious diseases in the same and other regions of the world experiencing conflict and violence., Competing Interests: The authors declare no competing interest.

Objective: The evolution of antibiotic resistance is far outpacing the development of new antibiotics, causing global public health concern about infections that will increasingly be unresponsive to antimicrobials. This risk of emerging antibiotic resistance may be meaningfully altered in highly AIDS-immunocompromised populations. Such populations fundamentally alter the bacterial evolutionary landscape in two ways, which we seek to model and analyze. First, widespread, population-level immunoincompetence creates a novel host environment with disrupted selective pressures. Second, within AIDS-prevalent populations, the recommendation that antibiotics be taken to treat and prevent opportunistic infection raises the risk of selection for drug-resistant pathogens., Design: To determine the impact of HIV/AIDS on the emergence of antibiotic resistance-specifically in the developing world where high prevalence and economic challenges complicate disease management., Methods: We present an SEIR epidemiological model of bacterial infection, and parametrize it to capture HIV/AIDS-attributable emergence of resistance under conditions of both high and low HIV/AIDS prevalence., Results: We demonstrate that HIV/AIDS-immunocompromised hosts can be responsible for a disproportionately greater contribution to emergence of resistance than would be expected based on population-wide HIV/AIDS prevalence alone., Conclusions: As such, the AIDS-immunocompromised have the potential become wellsprings of novel, resistant, opportunistic pathogen strains that can propagate into the broader global community. We discuss how public health policies for HIV/AIDS management can shape the evolutionary environment for opportunistic bacterial infections., Competing Interests: The authors have declared that no competing interests exist.

Background: Control of gambiense sleeping sickness relies predominantly on passive and active screening of people, followed by treatment., Methods: Mathematical modeling explores the potential of 3 complementary interventions in high- and low-transmission settings., Results: Intervention strategies that included vector control are predicted to halt transmission most quickly. Targeted active screening, with better and more focused coverage, and enhanced passive surveillance, with improved access to diagnosis and treatment, are both estimated to avert many new infections but, when used alone, are unlikely to halt transmission before 2030 in high-risk settings., Conclusions: There was general model consensus in the ranking of the 3 complementary interventions studied, although with discrepancies between the quantitative predictions due to differing epidemiological assumptions within the models. While these predictions provide generic insights into improving control, the most effective strategy in any situation depends on the specific epidemiology in the region and the associated costs.

Recent mathematical and statistical modeling of leprosy incidence data provides estimates of the current undiagnosed population and projections of diagnosed cases, as well as ongoing transmission. Furthermore, modeling studies have been used to evaluate the effectiveness of proposed intervention strategies, such as postleprosy exposure prophylaxis and novel diagnostics, relative to current approaches. Such modeling studies have revealed both a slow decline of new cases and a substantial pool of undiagnosed infections. These findings highlight the need for active case detection, particularly targeting leprosy foci, as well as for continued research into innovative accurate, rapid, and cost-effective diagnostics. As leprosy incidence continues to decline, targeted active case detection primarily in foci and connected areas will likely become increasingly important.

Incarcerated populations experience elevated burdens of infectious diseases, which are exacerbated by limited access to prevention measures. Dynamic models are used to assess the spread and control of diseases within correctional facilities and repercussions on the general population. Our systematic review of dynamic models of infectious diseases within correctional settings identified 34 studies published between 1996 and 2017. Of these, 23 focused on disease dynamics and intervention in prison without accounting for subsequent spread to the community. The main diseases modeled in these studies were human immunodeficiency virus (HIV; n = 14, 41%), tuberculosis (TB; n = 10, 29%), and hepatitis C virus (HCV; n = 7, 21%). Models were fitted to epidemiologic data in 14 studies; uncertainty and sensitivity analyses were conducted in 8, and validation of model projection against empirical data was done in 1 study. According to the models, prison-based screening and treatment may be highly effective strategies for reducing the burden of HIV, TB, HCV, and other sexually transmissible infections among prisoners and the general community. Decreasing incarceration rates were projected to reduce HIV and HCV infections among people who inject drugs and TB infections among all prisoners. Limitations of the modeling studies and opportunities for using dynamic models to develop quantitative evidence for informing prison infection control measures are discussed.

Introduction: Schistosomiasis is a chronic parasitic trematode disease that affects over 240 million people worldwide. The Schistosoma lifecycle is complex, involving transmission via specific intermediate-host freshwater snails. Predictive mathematical models of Schistosoma transmission have often chosen to simplify or ignore the details of environmental human-snail interaction in their analyses. Schistosome transmission models now aim to provide better precision for policy planning of elimination of transmission. This heightens the importance of including the environmental complexity of vector-pathogen interaction in order to make more accurate projections., Methodology and Principal Findings: We propose a nonlinear snail force of infection (FOI) that takes into account an intermediate larval stage (miracidium) and snail biology. We focused, in particular, on the effects of snail force of infection (FOI) on the impact of mass drug administration (MDA) in human communities. The proposed (modified) model was compared to a conventional model in terms of their predictions. A longitudinal dataset generated in Kenya field studies was used for model calibration and validation. For each sample community, we calibrated modified and conventional model systems, then used them to model outcomes for a range of MDA regimens. In most cases, the modified model predicted more vigorous post-MDA rebound, with faster relapse to baseline levels of infection. The effect was pronounced in higher risk communities. When compared to observed data, only the modified system was able to successfully predict persistent rebound of Schistosoma infection., Conclusion and Significance: The observed impact of varying location-specific snail inputs sheds light on the diverse MDA response patterns noted in operational research on schistosomiasis control, such as the recent SCORE project. Efficiency of human-to-snail transmission is likely to be much higher than predicted by standard models, which, in practice, will make local elimination by implementation of MDA alone highly unlikely, even over a multi-decade period., Competing Interests: The authors have declared that no competing interests exist.

Background: Mosquito-borne and sexually transmitted Zika virus has become widespread across Central and South America and the Caribbean. Many Zika vaccine candidates are under active development., Objective: To quantify the effect of Zika vaccine prioritization of females aged 9 to 49 years, followed by males aged 9 to 49 years, on incidence of prenatal Zika infections., Design: A compartmental model of Zika transmission between mosquitoes and humans was developed and calibrated to empirical estimates of country-specific mosquito density. Mosquitoes were stratified into susceptible, exposed, and infected groups; humans were stratified into susceptible, exposed, infected, recovered, and vaccinated groups. Age-specific fertility rates, Zika sexual transmission, and country-specific demographics were incorporated., Setting: 34 countries and territories in the Americas with documented Zika outbreaks., Target Population: Males and females aged 9 to 49 years., Intervention: Age- and sex-targeted immunization using a Zika vaccine with 75% efficacy., Measurements: Annual prenatal Zika infections., Results: For a base-case vaccine efficacy of 75% and vaccination coverage of 90%, immunizing females aged 9 to 49 years (the World Health Organization target population) would reduce the incidence of prenatal infections by at least 94%, depending on the country-specific Zika attack rate. In regions where an outbreak is not expected for at least 10 years, vaccination of women aged 15 to 29 years is more efficient than that of women aged 30 years or older., Limitation: Population-level modeling may not capture all local and neighborhood-level heterogeneity in mosquito abundance or Zika incidence., Conclusion: A Zika vaccine of moderate to high efficacy may virtually eliminate prenatal infections through a combination of direct protection and transmission reduction. Efficiency of age-specific targeting of Zika vaccination depends on the timing of future outbreaks., Primary Funding Source: National Institutes of Health.

Approaching disease elimination, it is crucial to be able to assess progress towards key objectives using quantitative tools. For Gambian human African trypanosomiasis (HAT), the ultimate goal is to stop transmission by 2030, while intermediary targets include elimination as a public health problem - defined as <1 new case per 10,000 inhabitants in 90% of foci, and <2000 reported cases by 2020. Using two independent mathematical models, this study assessed the achievability of these goals in the former Equateur province of the Democratic Republic of Congo, which historically had endemic levels of disease. The two deterministic models used different assumptions on disease progression, risk of infection and non-participation in screening, reflecting biological uncertainty. To validate the models a censor-fit-uncensor procedure was used to fit to health-zone level data from 2000 to 2012; initially the last six years were censored, then three and the final step utilised all data. The different model projections were used to evaluate the expected transmission and reporting for each health zone within each province under six intervention strategies using currently available tools. In 2012 there were 197 reported HAT cases in former Equateur reduced from 6828 in 2000, however this reflects lower active testing for HAT (1.3% of the population compared to 7.2%). Modelling results indicate that there are likely to be <300 reported cases in former Equateur in 2020 if screening continues at the mean level for 2000-2012 (6.2%), and <120 cases if vector control is introduced. Some health zones may fail to achieve <1 new case per 10,000 by 2020 without vector control, although most appear on track for this target using medical interventions alone. The full elimination goal will be harder to reach; between 39 and 54% of health zones analysed may have to improve their current medical-only strategy to stop transmission completely by 2030., (Copyright © 2017 The Authors. Published by Elsevier B.V. All rights reserved.)

Lymphatic Filariasis and Onchocerciasis (river blindness) constitute pressing public health issues in tropical regions. Global elimination programs, involving mass drug administration (MDA), have been launched by the World Health Organisation. Although the drugs used are generally well tolerated, individuals who are highly co-infected with Loa loa are at risk of experiencing serious adverse events. Highly infected individuals are more likely to be found in communities with high prevalence. An understanding of the relationship between individual infection and population-level prevalence can therefore inform decisions on whether MDA can be safely administered in an endemic community. Based on Loa loa infection intensity data from individuals in Cameroon, the Republic of the Congo and the Democratic Republic of the Congo we develop a statistical model for the distribution of infection levels in communities. We then use this model to make predictive inferences regarding the proportion of individuals whose parasite count exceeds policy-relevant levels. In particular we show how to exploit the positive correlation between community-level prevalence and intensity of infection in order to predict the proportion of highly infected individuals in a community given only prevalence data from the community in question. The resulting prediction intervals are not substantially wider, and in some cases narrower, than the corresponding binomial confidence intervals obtained from data that include measurements of individual infection levels. Therefore the model developed here facilitates the estimation of the proportion of individuals highly infected with Loa loa using only estimated community level prevalence. It can be used to assess the risk of rolling out MDA in a specific community, or to guide policy decisions., Competing Interests: The authors have declared that no competing interests exist.

Background: Because of the risk for Zika virus infection in the Americas and the links between infection and microcephaly, other serious neurologic conditions, and fetal death, health ministries across the region have advised women to delay pregnancy. However, the effectiveness of this policy in reducing prenatal Zika virus infection has yet to be quantified., Objective: To evaluate the effectiveness of pregnancy-delay policies on the incidence and prevalence of prenatal Zika virus infection., Design: Vector-borne Zika virus transmission model fitted to epidemiologic data from 2015 to 2016 on Zika virus infection in Colombia., Setting: Colombia, August 2015 to July 2017., Patients: Population of Colombia, stratified by sex, age, and pregnancy status., Intervention: Recommendations to delay pregnancy by 3, 6, 9, 12, or 24 months, at different levels of adherence., Measurements: Weekly and cumulative incidence of prenatal infections and microcephaly cases., Results: With 50% adherence to recommendations to delay pregnancy by 9 to 24 months, the cumulative incidence of prenatal Zika virus infections is likely to decrease by 17% to 44%, whereas recommendations to delay pregnancy by 6 or fewer months are likely to increase prenatal infections by 2% to 7%. This paradoxical exacerbation of prenatal Zika virus exposure is due to an elevated risk for pregnancies to shift toward the peak of the outbreak., Limitation: Sexual transmission was not explicitly accounted for in the model because of limited data but was implicitly subsumed within the overall transmission rate, which was calibrated to observed incidence., Conclusion: Pregnancy delays can have a substantial effect on reducing cases of microcephaly but risks exacerbating the Zika virus outbreak if the duration is not sufficient. Duration of the delay, population adherence, and the timing of initiation of the intervention must be carefully considered., Primary Funding Source: National Institutes of Health.

Background: As Zika virus continues to spread, decisions regarding resource allocations to control the outbreak underscore the need for a tool to weigh policies according to their cost and the health burden they could avert. For example, to combat the current Zika outbreak the US President requested the allocation of $1.8 billion from Congress in February 2016., Methodology/principal Findings: Illustrated through an interactive tool, we evaluated how the number of Zika cases averted, the period during pregnancy in which Zika infection poses a risk of microcephaly, and probabilities of microcephaly and Guillain-Barré Syndrome (GBS) impact the cost at which an intervention is cost-effective. From Northeast Brazilian microcephaly incidence data, we estimated the probability of microcephaly in infants born to Zika-infected women (0.49% to 2.10%). We also estimated the probability of GBS arising from Zika infections in Brazil (0.02% to 0.06%) and Colombia (0.08%). We calculated that each microcephaly and GBS case incurs the loss of 29.95 DALYs and 1.25 DALYs per case, as well as direct medical costs for Latin America and the Caribbean of $91,102 and $28,818, respectively. We demonstrated the utility of our cost-effectiveness tool with examples evaluating funding commitments by Costa Rica and Brazil, the US presidential proposal, and the novel approach of genetically modified mosquitoes. Our analyses indicate that the commitments and the proposal are likely to be cost-effective, whereas the cost-effectiveness of genetically modified mosquitoes depends on the country of implementation., Conclusions/significance: Current estimates from our tool suggest that the health burden from microcephaly and GBS warrants substantial expenditures focused on Zika virus control. Our results justify the funding committed in Costa Rica and Brazil and many aspects of the budget outlined in the US president's proposal. As data continue to be collected, new parameter estimates can be customized in real-time within our user-friendly tool to provide updated estimates on cost-effectiveness of interventions and inform policy decisions in country-specific settings.

Every year in the United States more than 12,000 women are diagnosed with cervical cancer, a disease principally caused by human papillomavirus (HPV). Bivalent and quadrivalent HPV vaccines protect against 66% of HPV-associated cervical cancers, and a new nonavalent vaccine protects against an additional 15% of cervical cancers. However, vaccination policy varies across states, and migration between states interdependently dilutes state-specific vaccination policies. To quantify the economic and epidemiological impacts of switching to the nonavalent vaccine both for individual states and for the nation as a whole, we developed a model of HPV transmission and cervical cancer incidence that incorporates state-specific demographic dynamics, sexual behavior, and migratory patterns. At the national level, the nonavalent vaccine was shown to be cost-effective compared with the bivalent and quadrivalent vaccines at any coverage despite the greater per-dose cost of the new vaccine. Furthermore, the nonavalent vaccine remains cost-effective with up to an additional 40% coverage of the adolescent population, representing 80% of girls and 62% of boys. We find that expansion of coverage would have the greatest health impact in states with the lowest coverage because of the decreasing marginal returns of herd immunity. Our results show that if policies promoting nonavalent vaccine implementation and expansion of coverage are coordinated across multiple states, all states benefit both in health and in economic terms.

Chikungunya, a re-emerging arbovirus transmitted to humans by Aedes aegypti and Ae. albopictus mosquitoes, causes debilitating disease characterized by an acute febrile phase and chronic joint pain. Chikungunya has recently spread to the island of St. Martin and subsequently throughout the Americas. The disease is now affecting 42 countries and territories throughout the Americas. While chikungunya is mainly a tropical disease, the recent introduction and subsequent spread of Ae. albopictus into temperate regions has increased the threat of chikungunya outbreaks beyond the tropics. Given that there are currently no vaccines or treatments for chikungunya, vector control remains the primary measure to curtail transmission. To investigate the effectiveness of a containment strategy that combines disease surveillance, localized vector control and transmission reduction measures, we developed a model of chikungunya transmission dynamics within a large residential neighborhood, explicitly accounting for human and mosquito movement. Our findings indicate that prompt targeted vector control efforts combined with measures to reduce transmission from symptomatic cases to mosquitoes may be highly effective approaches for controlling outbreaks of chikungunya, provided that sufficient detection of chikungunya cases can be achieved.

The 2014-2015 Ebola epidemic has been the most protracted and devastating in the history of the disease. To prevent future outbreaks on this scale, it is imperative to understand the reasons that led to eventual disease control. Here, we evaluated the shifts of Ebola dynamics at national and local scales during the epidemic in Liberia. We used a transmission model calibrated to epidemiological data between June 9 and December 31, 2014, to estimate the extent of community and hospital transmission. We found that despite varied local epidemic patterns, community transmission was reduced by 40-80% in all the counties analyzed. Our model suggests that the tapering of the epidemic was achieved through reductions in community transmission, rather than accumulation of immune individuals through asymptomatic infection and unreported cases. Although the times at which this transmission reduction occurred in the majority of the Liberian counties started before any large expansion in hospital capacity and the distribution of home protection kits, it remains difficult to associate the presence of interventions with reductions in Ebola incidence., (© The American Society of Tropical Medicine and Hygiene.)

Response to the 2014-2015 Ebola outbreak in West Africa overwhelmed the healthcare systems of Guinea, Liberia, and Sierra Leone, reducing access to health services for diagnosis and treatment for the major diseases that are endemic to the region: malaria, HIV/AIDS, and tuberculosis. To estimate the repercussions of the Ebola outbreak on the populations at risk for these diseases, we developed computational models for disease transmission and infection progression. We estimated that a 50% reduction in access to healthcare services during the Ebola outbreak exacerbated malaria, HIV/AIDS, and tuberculosis mortality rates by additional death counts of 6,269 (2,564-12,407) in Guinea; 1,535 (522-2,8780) in Liberia; and 2,819 (844-4,844) in Sierra Leone. The 2014-2015 Ebola outbreak was catastrophic in these countries, and its indirect impact of increasing the mortality rates of other diseases was also substantial.

Background: Human African Trypanosomiasis threatens human health across Africa. The subspecies T.b. gambiense is responsible for the vast majority of reported HAT cases. Over the past decade, expanded control efforts accomplished a substantial reduction in HAT transmission, spurring the WHO to include Gambian HAT on its roadmap for 2020 elimination. To inform the implementation of this elimination goal, we evaluated the likelihood that current control interventions will achieve the 2020 target in Boffa prefecture in Guinea, which has one of the highest prevalences for HAT in the country, and where vector control measures have been implemented in combination with the traditional screen and treat strategy., Methods: We developed a three-species mathematical model of HAT and used a Bayesian melding approach to calibrate the model to epidemiological and entomological data from Boffa. From the calibrated model, we generated the probabilistic predictions regarding the likelihood that the current HAT control programs could achieve elimination by 2020 in Boffa., Results: Our model projections indicate that if annual vector control is implemented in combination with annual or biennial active case detection and treatment, the probability of eliminating HAT as public health problem in Boffa by 2020 is over 90%. Annual implementation of vector control alone has a significant impact but a decreased chance of reaching the objective (77%). However, if the ongoing control efforts are interrupted, HAT will continue to remain a public health problem. In the presence of a non-human animal transmission reservoir, intervention strategies must be maintained at high coverage, even after 2020 elimination, to prevent HAT reemerging as a public health problem., Conclusions: Complementing active screening and treatment with vector control has the potential to achieve the elimination target before 2020 in the Boffa focus. However, surveillance must continue after elimination to prevent reemergence.

Using geo-referenced case data, we present spatial and spatio-temporal cluster analyses of the early spread of the 2013-2015 chikungunya virus (CHIKV) in Dominica, an island in the Caribbean. Spatial coordinates of the locations of the first 417 reported cases observed between December 15th, 2013 and March 11th, 2014, were captured using the Global Positioning System (GPS). We observed a preponderance of female cases, which has been reported for CHIKV outbreaks in other regions. We also noted statistically significant spatial and spatio-temporal clusters in highly populated areas and observed major clusters prior to implementation of intensive vector control programs suggesting early vector control measures, and education had an impact on the spread of the CHIKV epidemic in Dominica. A dynamical identification of clusters can lead to local assessment of risk and provide opportunities for targeted control efforts for nations experiencing CHIKV outbreaks.

As a devastating Ebola outbreak in West Africa continues, non-pharmaceutical control measures including contact tracing, quarantine, and case isolation are being implemented. In addition, public health agencies are scaling up efforts to test and deploy candidate vaccines. Given the experimental nature and limited initial supplies of vaccines, a mass vaccination campaign might not be feasible. However, ring vaccination of likely case contacts could provide an effective alternative in distributing the vaccine. To evaluate ring vaccination as a strategy for eliminating Ebola, we developed a pair approximation model of Ebola transmission, parameterized by confirmed incidence data from June 2014 to January 2015 in Liberia and Sierra Leone. Our results suggest that if a combined intervention of case isolation and ring vaccination had been initiated in the early fall of 2014, up to an additional 126 cases in Liberia and 560 cases in Sierra Leone could have been averted beyond case isolation alone. The marginal benefit of ring vaccination is predicted to be greatest in settings where there are more contacts per individual, greater clustering among individuals, when contact tracing has low efficacy or vaccination confers post-exposure protection. In such settings, ring vaccination can avert up to an additional 8% of Ebola cases. Accordingly, ring vaccination is predicted to offer a moderately beneficial supplement to ongoing non-pharmaceutical Ebola control efforts.

Artemisinin-based combination therapies (ACTs) are currently considered the first-line treatments for uncomplicated Plasmodium falciparum malaria. Among these, artemether-lumefantrine (AL) has been the most widely prescribed ACT in sub-Saharan Africa. Recent clinical trials conducted in sub-Saharan Africa have shown that dihydroartemisinin-piperaquine (DP), a most recent ACT, may have a longer post-treatment prophylactic period and post-treatment infection period (duration of gametocyte carriage) than AL. Using epidemiological and clinical data on the efficacy of AL and DP, we developed and parameterized a mathematical transmission model that we used to compare the population-level impact of AL and DP for reducing P. falciparum malaria transmission in sub-Saharan Africa. Our results showed that DP is likely to more effectively reduce malaria incidence of clinical episodes than AL. However in low P. falciparum transmission areas, DP and AL are likely to be equally effective in reducing malaria prevalence. The predictions of our model were shown to be robust to the empirical uncertainty summarizing the epidemiological parameters. DP should be considered as a replacement for AL as first-line treatment of uncomplicated malaria in highly endemic P. falciparum communities. To optimize the effectiveness of ACTs, it is necessary to tailor treatment policies to the transmission intensity in different settings., (© The American Society of Tropical Medicine and Hygiene.)

Background: The Ebola outbreak that is sweeping across West Africa is the largest, most volatile, and deadliest Ebola epidemic ever recorded. Liberia is the most profoundly affected country, with more than 3500 infections and 2000 deaths recorded in the past 3 months., Objective: To evaluate the contribution of disease progression and case fatality on transmission and to examine the potential for targeted interventions to eliminate the disease., Design: Stochastic transmission model that integrates epidemiologic and clinical data on incidence and case fatality, daily viral load among survivors and nonsurvivors evaluated on the basis of the 2000-2001 outbreak in Uganda, and primary data on contacts of patients with Ebola in Liberia., Setting: Montserrado County, Liberia, July to September 2014., Measurements: Ebola incidence and case-fatality records from 2014 Liberian Ministry of Health and Social Welfare., Results: The average number of secondary infections generated throughout the entire infectious period of a single infected case, R, was estimated as 1.73 (95% CI, 1.66 to 1.83). There was substantial stratification between survivors (RSurvivors), for whom the estimate was 0.66 (CI, 0.10 to 1.69), and nonsurvivors (RNonsurvivors), for whom the estimate was 2.36 (CI, 1.72 to 2.80). The nonsurvivors had the highest risk for transmitting the virus later in the course of disease progression. Consequently, the isolation of 75% of infected individuals in critical condition within 4 days from symptom onset has a high chance of eliminating the disease., Limitation: Projections are based on the initial dynamics of the epidemic, which may change as the outbreak and interventions evolve., Conclusion: These results underscore the importance of isolating the most severely ill patients with Ebola within the first few days of their symptomatic phase., Primary Funding Source: National Institutes of Health.

Background: A substantial scale-up in public health response is needed to control the unprecedented Ebola virus disease (EVD) epidemic in west Africa. Current international commitments seek to expand intervention capacity in three areas: new EVD treatment centres, case ascertainment through contact tracing, and household protective kit allocation. We aimed to assess how these interventions could be applied individually and in combination to avert future EVD cases and deaths., Methods: We developed a transmission model of Ebola virus that we fitted to reported EVD cases and deaths in Montserrado County, Liberia. We used this model to assess the effectiveness of expanding EVD treatment centres, increasing case ascertainment, and allocating protective kits for controlling the outbreak in Montserrado. We varied the efficacy of protective kits from 10% to 50%. We compared intervention initiation on Oct 15, 2014, Oct 31, 2014, and Nov 15, 2014. The status quo intervention was defined in terms of case ascertainment and capacity of EVD treatment centres on Sept 23, 2014, and all behaviour and contact patterns relevant to transmission as they were occurring at that time. The primary outcome measure was the expected number of cases averted by Dec 15, 2014., Findings: We estimated the basic reproductive number for EVD in Montserrado to be 2·49 (95% CI 2·38-2·60). We expect that allocating 4800 additional beds at EVD treatment centres and increasing case ascertainment five-fold in November, 2014, can avert 77 312 (95% CI 68 400-85 870) cases of EVD relative to the status quo by Dec 15, 2014. Complementing these measures with protective kit allocation raises the expectation as high as 97 940 (90 096-105 606) EVD cases. If deployed by Oct 15, 2014, equivalent interventions would have been expected to avert 137 432 (129 736-145 874) cases of EVD. If delayed to Nov 15, 2014, we expect the interventions will at best avert 53 957 (46 963-60 490) EVD cases., Interpretation: The number of beds at EVD treatment centres needed to effectively control EVD in Montserrado substantially exceeds the 1700 pledged by the USA to west Africa. Accelerated case ascertainment is needed to maximise effectiveness of expanding the capacity of EVD treatment centres. Distributing protective kits can further augment prevention of EVD, but it is not an adequate stand-alone measure for controlling the outbreak. Our findings highlight the rapidly closing window of opportunity for controlling the outbreak and averting a catastrophic toll of EVD cases and deaths., Funding: US National Institutes of Health., (Copyright © 2014 Elsevier Ltd. All rights reserved.)

The ongoing Ebola outbreak poses an alarming risk to the countries of West Africa and beyond. To assess the effectiveness of containment strategies, we developed a stochastic model of Ebola transmission between and within the general community, hospitals, and funerals, calibrated to incidence data from Liberia. We find that a combined approach of case isolation, contact-tracing with quarantine, and sanitary funeral practices must be implemented with utmost urgency in order to reverse the growth of the outbreak. As of 19 September, under status quo, our model predicts that the epidemic will continue to spread, generating a predicted 224 (134 to 358) daily cases by 1 December, 280 (184 to 441) by 15 December, and 348 (249 to 545) by 30 December., (Copyright © 2014, American Association for the Advancement of Science.)

Background: Sub-Saharan Africa harbors the majority of the global burden of malaria and schistosomiasis infections. The co-endemicity of these two tropical diseases has prompted investigation into the mechanisms of coinfection, particularly the competing immunological responses associated with each disease. Epidemiological studies have shown that infection with Schistosoma mansoni is associated with a greater malaria incidence among school-age children., Methodology: We developed a co-epidemic model of malaria and S. mansoni transmission dynamics which takes into account key epidemiological interaction between the two diseases in terms of elevated malaria incidence among individuals with S. mansoni high egg output. The model was parameterized for S. mansoni high-risk endemic communities, using epidemiological and clinical data of the interaction between S. mansoni and malaria among children in sub-Saharan Africa. We evaluated the potential impact of the S. mansoni-malaria interaction and mass treatment of schistosomiasis on malaria prevalence in co-endemic communities., Principal Findings: Our results suggest that in the absence of mass drug administration of praziquantel, the interaction between S. mansoni and malaria may reduce the effectiveness of malaria treatment for curtailing malaria transmission, in S. mansoni high-risk endemic communities. However, when malaria treatment is used in combination with praziquantel, mass praziquantel administration may increase the effectiveness of malaria control intervention strategy for reducing malaria prevalence in malaria- S. mansoni co-endemic communities., Conclusions/significance: Schistosomiasis treatment and control programmes in regions where S. mansoni and malaria are highly prevalent may have indirect benefits on reducing malaria transmission as a result of disease interactions. In particular, mass praziquantel administration may not only have the direct benefit of reducing schistosomiasis infection, it may also reduce malaria transmission and disease burden.