The role of wastewater-based epidemiology for SARS-CoV-2 in developing countries: Cumulative evidence from South Africa supports sentinel site surveillance to guide public health decision-making

The uptake of wastewater-based epidemiology (WBE) for SARS-CoV-2 as a complementary tool for monitoring population-level epidemiological features of the COVID-19 pandemic in low-and-middle-income countries (LMICs) is low. We report on the findings from the South African SARS-CoV-2 WBE surveillance network and make recommendations regarding the implementation of WBE in LMICs. Eight laboratories quantified influent wastewater collected from 87 wastewater treatment plants in all nine South African provinces from 01 June 2021 to 31 May 2022 inclusive, during the 3rd and 4th waves of COVID-19. Correlation and regression analyses between wastewater levels of SARS-CoV-2 and district laboratory-confirmed caseloads were conducted. The sensitivity and specificity of novel 'rules' based on WBE data to predict an epidemic wave were determined. Amongst 2158 wastewater samples, 543/648 (85 %) samples taken during a wave tested positive for SARS-CoV-2 compared with 842 positive tests from 1512 (55 %) samples taken during the interwave period. Overall, the regression-co-efficient was 0,66 (95 % confidence interval = 0,6-0,72, R2 = 0.59), ranging from 0.14 to 0.87 by testing laboratory. Early warning of the 4th wave of SARS-CoV-2 in Gauteng Province in November-December 2021 was demonstrated. A 50 % increase in log copies of SARS-CoV-2 compared with a rolling mean over the previous five weeks was the most sensitive predictive rule (58 %) to predict a new wave. Our findings support investment in WBE for SARS-CoV-2 surveillance in LMICs as an early warning tool. Standardising test methodology is necessary due to varying correlation strengths across laboratories and redundancy across testing plants. A sentinel site model can be used for surveillance networks without affecting WBE finding for decision-making. Further research is needed to identify optimal test frequency and the need for normalisation to population size to identify predictive and interpretive rules to support early warning and public health action.

Modelling outbreak response impact in human vaccine-preventable diseases: A systematic review of differences in practices between collaboration types before COVID-19

BACKGROUND

Outbreak response modelling often involves collaboration among academics, and experts from governmental and non-governmental organizations. We conducted a systematic review of modelling studies on human vaccine-preventable disease (VPD) outbreaks to identify patterns in modelling practices between two collaboration types. We complemented this with a mini comparison of foot-and-mouth disease (FMD), a veterinary disease that is controllable by vaccination.

METHODS

We searched three databases for modelling studies that assessed the impact of an outbreak response. We extracted data on author affiliation type (academic institution, governmental, and non-governmental organizations), location studied, and whether at least one author was affiliated to the studied location. We also extracted the outcomes and interventions studied, and model characteristics. Included studies were grouped into two collaboration types: purely academic (papers with only academic affiliations), and mixed (all other combinations) to help investigate differences in modelling patterns between collaboration types in the human disease literature and overall differences with FMD collaboration practices.

RESULTS

Human VPDs formed 227 of 252 included studies. Purely academic collaborations dominated the human disease studies (56%). Notably, mixed collaborations increased in the last seven years (2013-2019). Most studies had an author affiliated to an institution in the country studied (75.2%) but this was more likely among the mixed collaborations. Contrasted to the human VPDs, mixed collaborations dominated the FMD literature (56%). Furthermore, FMD studies more often had an author with an affiliation to the country studied (92%) and used complex model design, including stochasticity, and model parametrization and validation.

CONCLUSION

The increase in mixed collaboration studies over the past seven years could suggest an increase in the uptake of modelling for outbreak response decision-making. We encourage more mixed collaborations between academic and non-academic institutions and the involvement of locally affiliated authors to help ensure that the studies suit local contexts.

The National COVID-19 Epi Model (NCEM): Estimating cases, admissions and deaths for the first wave of COVID-19 in South Africa

In March 2020 the South African COVID-19 Modelling Consortium was formed to support government planning for COVID-19 cases and related healthcare. Models were developed jointly by local disease modelling groups to estimate cases, resource needs and deaths due to COVID-19. The National COVID-19 Epi Model (NCEM) while initially developed as a deterministic compartmental model of SARS-Cov-2 transmission in the nine provinces of South Africa, was adapted several times over the course of the first wave of infection in response to emerging local data and changing needs of government. By the end of the first wave, the NCEM had developed into a stochastic, spatially-explicit compartmental transmission model to estimate the total and reported incidence of COVID-19 across the 52 districts of South Africa. The model adopted a generalised Susceptible-Exposed-Infectious-Removed structure that accounted for the clinical profile of SARS-COV-2 (asymptomatic, mild, severe and critical cases) and avenues of treatment access (outpatient, and hospitalisation in non-ICU and ICU wards). Between end-March and early September 2020, the model was updated 11 times with four key releases to generate new sets of projections and scenario analyses to be shared with planners in the national and provincial Departments of Health, the National Treasury and other partners. Updates to model structure included finer spatial granularity, limited access to treatment, and the inclusion of behavioural heterogeneity in relation to the adoption of Public Health and Social Measures. These updates were made in response to local data and knowledge and the changing needs of the planners. The NCEM attempted to incorporate a high level of local data to contextualise the model appropriately to address South Africa's population and health system characteristics that played a vital role in producing and updating estimates of resource needs, demonstrating the importance of harnessing and developing local modelling capacity.

Human Exposure to Bats, Rodents and Monkeys in Bangladesh

Bats, rodents and monkeys are reservoirs for emerging zoonotic infections. We sought to describe the frequency of human exposure to these animals and the seasonal and geographic variation of these exposures in Bangladesh. During 2013-2016, we conducted a cross-sectional survey in a nationally representative sample of 10,002 households from 1001 randomly selected communities. We interviewed household members about exposures to bats, rodents and monkeys, including a key human-bat interface-raw date palm sap consumption. Respondents reported observing rodents (90%), bats (52%) and monkeys (2%) in or around their households, although fewer reported direct contact. The presence of monkeys around the household was reported more often in Sylhet division (7%) compared to other divisions. Households in Khulna (17%) and Rajshahi (13%) were more likely to report drinking date palm sap than in other divisions (1.5-5.6%). Date palm sap was mostly consumed during winter with higher frequencies in January (16%) and February (12%) than in other months (0-5.6%). There was a decreasing trend in drinking sap over the three years. Overall, we observed substantial geographic and seasonal patterns in human exposure to animals that could be sources of zoonotic disease. These findings could facilitate targeting emerging zoonoses surveillance, research and prevention efforts to areas and seasons with the highest levels of exposure.

Direct and Indirect Effects of the Coronavirus Disease 2019 Pandemic on Private Healthcare Utilization in South Africa, March 2020-September 2021

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic caused severe disruptions to healthcare in many areas of the world, but data remain scarce for sub-Saharan Africa.

METHODS

We evaluated trends in hospital admissions and outpatient emergency department (ED) and general practitioner (GP) visits to South Africa's largest private healthcare system during 2016-2021. We fit time series models to historical data and, for March 2020-September 2021, quantified changes in encounters relative to baseline.

RESULTS

The nationwide lockdown on 27 March 2020 led to sharp reductions in care-seeking behavior that persisted for 18 months after initial declines. For example, total admissions dropped 59.6% (95% confidence interval [CI], 52.4-66.8) during home confinement and were 33.2% (95% CI, 29-37.4) below baseline in September 2021. We identified 3 waves of all-cause respiratory encounters consistent with COVID-19 activity. Intestinal infections and non-COVID-19 respiratory illnesses experienced the most pronounced declines, with some diagnoses reduced 80%, even as nonpharmaceutical interventions (NPIs) relaxed. Non-respiratory hospitalizations, including injuries and acute illnesses, were 20%-60% below baseline throughout the pandemic and exhibited strong temporal associations with NPIs and mobility. ED attendances exhibited trends similar to those for hospitalizations, while GP visits were less impacted and have returned to pre-pandemic levels.

CONCLUSIONS

We found substantially reduced use of health services during the pandemic for a range of conditions unrelated to COVID-19. Persistent declines in hospitalizations and ED visits indicate that high-risk patients are still delaying seeking care, which could lead to morbidity or mortality increases in the future.

Estimating the time-varying reproduction number for COVID-19 in South Africa during the first four waves using multiple measures of incidence for public and private sectors across four waves

Objectives

We aimed to quantify transmission trends in South Africa during the first four waves of the COVID-19 pandemic using estimates of the time-varying reproduction number (R) and to compare the robustness of R estimates based on three different data sources and using data from public and private sector service providers.

Methods

We estimated R from March 2020 through April 2022, nationally and by province, based on time series of rt-PCR-confirmed cases, hospitalizations, and hospital-associated deaths, using a method which models daily incidence as a weighted sum of past incidence. We also estimated R separately using public and private sector data.

Results

Nationally, the maximum case-based R following the introduction of lockdown measures was 1.55 (CI: 1.43-1.66), 1.56 (CI: 1.47-1.64), 1.46 (CI: 1.38-1.53) and 3.33 (CI: 2.84-3.97) during the first (Wuhan-Hu), second (Beta), third (Delta), and fourth (Omicron) waves respectively. Estimates based on the three data sources (cases, hospitalisations, deaths) were generally similar during the first three waves but case-based estimates were higher during the fourth wave. Public and private sector R estimates were generally similar except during the initial lockdowns and in case-based estimates during the fourth wave.

Discussion

Agreement between R estimates using different data sources during the first three waves suggests that data from any of these sources could be used in the early stages of a future pandemic. High R estimates for Omicron relative to earlier waves is interesting given a high level of exposure pre-Omicron. The agreement between public and private sector R estimates highlights the fact that clients of the public and private sectors did not experience two separate epidemics, except perhaps to a limited extent during the strictest lockdowns in the first wave.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

Increased risk of SARS-CoV-2 reinfection associated with emergence of Omicron in South Africa

We provide two methods for monitoring reinfection trends in routine surveillance data to identify signatures of changes in reinfection risk and apply these approaches to data from South Africa's severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) epidemic to date. Although we found no evidence of increased reinfection risk associated with circulation of the Beta (B.1.351) or Delta (B.1.617.2) variants, we did find clear, population-level evidence to suggest immune evasion by the Omicron (B.1.1.529) variant in previously infected individuals in South Africa. Reinfections occurring between 1 November 2021 and 31 January 2022 were detected in individuals infected in all three previous waves, and there has been an increase in the risk of having a third infection since mid-November 2021.

Modelling the relative benefits of using the measles vaccine outside cold chain for outbreak response

INTRODUCTION

Rapid outbreak response vaccination is a strategy for measles control and elimination. Measles vaccines must be stored and transported within a specified temperature range, but this can present significant challenges when targeting remote populations. Measles vaccine licensure for delivery outside cold chain (OCC) could provide more vaccine transport/storage space without ice packs, and a solution to shorten response times. However, due to vaccine safety and wastage considerations, the OCC strategy will require other operational changes, potentially including the use of 1-dose (monodose) instead of 10-dose vials, requiring larger transport/storage equipment currently achieved with 10-dose vials. These trade-offs require quantitative comparisons of vaccine delivery options to evaluate their relative benefits.

METHODS

We developed a modelling framework combining elements of the vaccine supply chain - cold chain, vial, team, and transport equipment types - with a measles transmission dynamics model to compare vaccine delivery options. We compared 10 strategies resulting from combinations of the vaccine supply elements and grouped into three main classes: OCC, partial cold chain (PCC), and full cold chain (FCC). For each strategy, we explored a campaign with 20 teams sequentially targeting 5 locations with 100,000 individuals each. We characterised the time needed to freeze ice packs and complete the campaign (campaign duration), vaccination coverage, and cases averted, assuming a fixed pre-deployment delay before campaign commencement. We performed sensitivity analyses of the pre-deployment delay, population sizes, and two team allocation schemes.

RESULTS

The OCC, PCC, and FCC strategies achieve campaign durations of 50, 51, and 52 days, respectively. Nine of the ten strategies can achieve a vaccination coverage of 80%, and OCC averts the most cases.

DISCUSSION

The OCC strategy, therefore, presents improved operational and epidemiological outcomes relative to current practice and the other options considered.

Strategic testing approaches for targeted disease monitoring can be used to inform pandemic decision-making

More than 1.6 million Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) tests were administered daily in the United States at the peak of the epidemic, with a significant focus on individual treatment. Here, we show that objective-driven, strategic sampling designs and analyses can maximize information gain at the population level, which is necessary to increase situational awareness and predict, prepare for, and respond to a pandemic, while also continuing to inform individual treatment. By focusing on specific objectives such as individual treatment or disease prediction and control (e.g., via the collection of population-level statistics to inform lockdown measures or vaccine rollout) and drawing from the literature on capture-recapture methods to deal with nonrandom sampling and testing errors, we illustrate how public health objectives can be achieved even with limited test availability when testing programs are designed a priori to meet those objectives.

Pre-vaccination testing could expand coverage of two-dose COVID vaccines

Recent evidence indicates that a single dose of mRNA-based vaccines produce similar immune responses in people with evidence of past infection compared with two doses in immunologically naive individuals. For COVID-19 vaccines with two dose regimens, point-of-care antibody testing for prior infection when administering the first dose could enable expanded vaccine access in a cost-effective manner. Generally, antibody tests with sensitivity and specificity well below that typically accepted for product licensure would still enable expanded vaccine coverage, though to be cost-beneficial total test cost (i.e. procurement and administration) needs to be less than roughly a third of total vaccine dose cost. For highly sensitive (90%) and specific (99%) tests, coverage could be expanded by more than 33%. Tests with the appropriate performance characteristics are plausible, though likely need setting specific tailoring.

The Role of Remdesivir in South Africa: Preventing COVID-19 Deaths Through Increasing Intensive Care Unit Capacity

Countries such as South Africa have limited intensive care unit (ICU) capacity to handle the expected number of patients with COVID-19 requiring ICU care. Remdesivir can prevent deaths in countries such as South Africa by decreasing the number of days people spend in ICU, therefore freeing up ICU bed capacity.

African based researchers' output on models for the transmission dynamics of infectious diseases and public health interventions: A scoping review

Background

Applied epidemiological models are used in predicting future trends of diseases, for the basic understanding of disease and health dynamics, and to improve the measurement of health indicators. Mapping the research outputs of epidemiological modelling studies concerned with transmission dynamics of infectious diseases and public health interventions in Africa will help to identify the areas with substantial levels of research activities, areas with gaps, and research output trends.

Methods

A scoping review of applied epidemiological models of infectious disease studies that involved first or last authors affiliated to African institutions was conducted. Eligible studies were those concerned with the transmission dynamics of infectious diseases and public health interventions. The review was consistent with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) extension for scoping reviews. Four electronic databases were searched for peer-reviewed publications up to the end of April 2020.

Results

Of the 5927 publications identified, 181 met the inclusion criteria. The review identified 143 publications with first authors having an African institutional affiliation (AIA), while 81 had both first and last authors with an AIA. The publication authors were found to be predominantly affiliated with institutions based in South Africa and Kenya. Furthermore, human immunodeficiency virus, malaria, tuberculosis, and Ebola virus disease were found to be the most researched infectious diseases. There has been a gradual increase in research productivity across Africa especially in the last ten years, with several collaborative efforts spread both within and beyond Africa.

Conclusions

Research productivity in applied epidemiological modelling studies of infectious diseases may have increased, but there remains an under-representation of African researchers as leading authors. The study findings indicate a need for the development of research capacity through supporting existing institutions in Africa and promoting research funding that will address local health priorities.

Cost-effectiveness of Remdesivir and Dexamethasone for COVID-19 Treatment in South Africa

BACKGROUND

Dexamethasone and remdesivir have the potential to reduce coronavirus disease 2019 (COVID)-related mortality or recovery time, but their cost-effectiveness in countries with limited intensive care resources is unknown.

METHODS

We projected intensive care unit (ICU) needs and capacity from August 2020 to January 2021 using the South African National COVID-19 Epi Model. We assessed the cost-effectiveness of (1) administration of dexamethasone to ventilated patients and remdesivir to nonventilated patients, (2) dexamethasone alone to both nonventilated and ventilated patients, (3) remdesivir to nonventilated patients only, and (4) dexamethasone to ventilated patients only, all relative to a scenario of standard care. We estimated costs from the health care system perspective in 2020 US dollars, deaths averted, and the incremental cost-effectiveness ratios of each scenario.

RESULTS

Remdesivir for nonventilated patients and dexamethasone for ventilated patients was estimated to result in 408 (uncertainty range, 229-1891) deaths averted (assuming no efficacy [uncertainty range, 0%-70%] of remdesivir) compared with standard care and to save $15 million. This result was driven by the efficacy of dexamethasone and the reduction of ICU-time required for patients treated with remdesivir. The scenario of dexamethasone alone for nonventilated and ventilated patients requires an additional $159 000 and averts 689 [uncertainty range, 330-1118] deaths, resulting in $231 per death averted, relative to standard care.

CONCLUSIONS

The use of remdesivir for nonventilated patients and dexamethasone for ventilated patients is likely to be cost-saving compared with standard care by reducing ICU days. Further efforts to improve recovery time with remdesivir and dexamethasone in ICUs could save lives and costs in South Africa.

Changing Contact Patterns Over Disease Progression: Nipah Virus as a Case Study

Contact patterns play a key role in disease transmission, and variation in contacts during the course of illness can influence transmission, particularly when accompanied by changes in host infectiousness. We used surveys among 1642 contacts of 94 Nipah virus case patients in Bangladesh to determine how contact patterns (physical and with bodily fluids) changed as disease progressed in severity. The number of contacts increased with severity and, for case patients who died, peaked on the day of death. Given transmission has only been observed among fatal cases of Nipah virus infection, our findings suggest that changes in contact patterns during illness contribute to risk of infection.

Outbreak response intervention models of vaccine-preventable diseases in humans and foot-and-mouth disease in livestock: a protocol for a systematic review

INTRODUCTION

Outbreaks of vaccine-preventable diseases continue to threaten public health, despite the proven effectiveness of vaccines. Interventions such as vaccination, social distancing and palliative care are usually implemented, either individually or in combination, to control these outbreaks. Mathematical models are often used to assess the impact of these interventions and for supporting outbreak response decision making. The objectives of this systematic review, which covers all human vaccine-preventable diseases, are to determine the relative impact of vaccination compared with other outbreak interventions, and to ascertain the temporal trends in the use of modelling in outbreak response decision making. We will also identify gaps and opportunities for future research through a comparison with the foot-and-mouth disease outbreak response modelling literature, which has good examples of the use of modelling to inform outbreak response intervention decision making.

METHODS AND ANALYSIS

We searched on PubMed, Scopus, Web of Science, Google Scholar and some preprint servers from the start of indexing to 15 January 2020. Inclusion: modelling studies, published in English, that use a mechanistic approach to evaluate the impact of an outbreak intervention. Exclusion: reviews, and studies that do not describe or use mechanistic models or do not describe an outbreak. We will extract data from the included studies such as their objectives, model types and composition, and conclusions on the impact of the intervention. We will ascertain the impact of models on outbreak response decision making through visualisation of time trends in the use of the models. We will also present our results in narrative style.

ETHICS AND DISSEMINATION

This systematic review will not require any ethics approval since it only involves scientific articles. The review will be disseminated in a peer-reviewed journal and at various conferences fitting its scope.

PROSPERO REGISTRATION NUMBER

CRD42020160803.

Cost-effectiveness of remdesivir and dexamethasone for COVID-19 treatment in South Africa

Background South Africa recently experienced a first peak in COVID-19 cases and mortality. Dexamethasone and remdesivir both have the potential to reduce COVID-related mortality, but their cost-effectiveness in a resource-limited setting with scant intensive care resources is unknown. Methods We projected intensive care unit (ICU) needs and capacity from August 2020 to January 2021 using the South African National COVID-19 Epi Model. We assessed cost-effectiveness of 1) administration of dexamethasone to ventilated patients and remdesivir to non-ventilated patients, 2) dexamethasone alone to both non-ventilated and ventilated patients, 3) remdesivir to non-ventilated patients only, and 4) dexamethasone to ventilated patients only; all relative to a scenario of standard care. We estimated costs from the healthcare system perspective in 2020 USD, deaths averted, and the incremental cost effectiveness ratios of each scenario. Results Remdesivir for non-ventilated patients and dexamethasone for ventilated patients was estimated to result in 1,111 deaths averted (assuming a 0-30% efficacy of remdesivir) compared to standard care, and save $11.5 million. The result was driven by the efficacy of the drugs, and the reduction of ICU-time required for patients treated with remdesivir. The scenario of dexamethasone alone to ventilated and non-ventilated patients requires additional $159,000 and averts 1,146 deaths, resulting in $139 per death averted, relative to standard care. Conclusions The use of dexamethasone for ventilated and remdesivir for non-ventilated patients is likely to be cost-saving compared to standard care. Given the economic and health benefits of both drugs, efforts to ensure access to these medications is paramount.

Key questions for modelling COVID-19 exit strategies

Combinations of intense non-pharmaceutical interventions (lockdowns) were introduced worldwide to reduce SARS-CoV-2 transmission. Many governments have begun to implement exit strategies that relax restrictions while attempting to control the risk of a surge in cases. Mathematical modelling has played a central role in guiding interventions, but the challenge of designing optimal exit strategies in the face of ongoing transmission is unprecedented. Here, we report discussions from the Isaac Newton Institute 'Models for an exit strategy' workshop (11-15 May 2020). A diverse community of modellers who are providing evidence to governments worldwide were asked to identify the main questions that, if answered, would allow for more accurate predictions of the effects of different exit strategies. Based on these questions, we propose a roadmap to facilitate the development of reliable models to guide exit strategies. This roadmap requires a global collaborative effort from the scientific community and policymakers, and has three parts: (i) improve estimation of key epidemiological parameters; (ii) understand sources of heterogeneity in populations; and (iii) focus on requirements for data collection, particularly in low-to-middle-income countries. This will provide important information for planning exit strategies that balance socio-economic benefits with public health.

Ecological interventions to prevent and manage zoonotic pathogen spillover

Spillover of a pathogen from a wildlife reservoir into a human or livestock host requires the pathogen to overcome a hierarchical series of barriers. Interventions aimed at one or more of these barriers may be able to prevent the occurrence of spillover. Here, we demonstrate how interventions that target the ecological context in which spillover occurs (i.e. ecological interventions) can complement conventional approaches like vaccination, treatment, disinfection and chemical control. Accelerating spillover owing to environmental change requires effective, affordable, durable and scalable solutions that fully harness the complex processes involved in cross-species pathogen spillover.

This article is part of the theme issue ‘Dynamic and integrative approaches to understanding pathogen spillover’.

Transmission of Nipah Virus - 14 Years of Investigations in Bangladesh

BACKGROUND

Nipah virus is a highly virulent zoonotic pathogen that can be transmitted between humans. Understanding the dynamics of person-to-person transmission is key to designing effective interventions.

METHODS

We used data from all Nipah virus cases identified during outbreak investigations in Bangladesh from April 2001 through April 2014 to investigate case-patient characteristics associated with onward transmission and factors associated with the risk of infection among patient contacts.

RESULTS

Of 248 Nipah virus cases identified, 82 were caused by person-to-person transmission, corresponding to a reproduction number (i.e., the average number of secondary cases per case patient) of 0.33 (95% confidence interval [CI], 0.19 to 0.59). The predicted reproduction number increased with the case patient's age and was highest among patients 45 years of age or older who had difficulty breathing (1.1; 95% CI, 0.4 to 3.2). Case patients who did not have difficulty breathing infected 0.05 times as many contacts (95% CI, 0.01 to 0.3) as other case patients did. Serologic testing of 1863 asymptomatic contacts revealed no infections. Spouses of case patients were more often infected (8 of 56 [14%]) than other close family members (7 of 547 [1.3%]) or other contacts (18 of 1996 [0.9%]). The risk of infection increased with increased duration of exposure of the contacts (adjusted odds ratio for exposure of >48 hours vs. ≤1 hour, 13; 95% CI, 2.6 to 62) and with exposure to body fluids (adjusted odds ratio, 4.3; 95% CI, 1.6 to 11).

CONCLUSIONS

Increasing age and respiratory symptoms were indicators of infectivity of Nipah virus. Interventions to control person-to-person transmission should aim to reduce exposure to body fluids. (Funded by the National Institutes of Health and others.).

Emerging infectious diseases and biological invasions: a call for a One Health collaboration in science and management

The study and management of emerging infectious diseases (EIDs) and of biological invasions both address the ecology of human-associated biological phenomena in a rapidly changing world. However, the two fields work mostly in parallel rather than in concert. This review explores how the general phenomenon of an organism rapidly increasing in range or abundance is caused, highlights the similarities and differences between research on EIDs and invasions, and discusses shared management insights and approaches. EIDs can arise by: (i) crossing geographical barriers due to human-mediated dispersal, (ii) crossing compatibility barriers due to evolution, and (iii) lifting of environmental barriers due to environmental change. All these processes can be implicated in biological invasions, but only the first defines them. Research on EIDs is embedded within the One Health concept-the notion that human, animal and ecosystem health are interrelated and that holistic approaches encompassing all three components are needed to respond to threats to human well-being. We argue that for sustainable development, biological invasions should be explicitly considered within One Health. Management goals for the fields are the same, and direct collaborations between invasion scientists, disease ecologists and epidemiologists on modelling, risk assessment, monitoring and management would be mutually beneficial.

Epidemic curves made easy using the R package incidence

The epidemiological curve (epicurve) is one of the simplest yet most useful tools used by field epidemiologists, modellers, and decision makers for assessing the dynamics of infectious disease epidemics. Here, we present the free, open-source package incidence for the R programming language, which allows users to easily compute, handle, and visualise epicurves from unaggregated linelist data. This package was built in accordance with the development guidelines of the R Epidemics Consortium (RECON), which aim to ensure robustness and reliability through extensive automated testing, documentation, and good coding practices. As such, it fills an important gap in the toolbox for outbreak analytics using the R software, and provides a solid building block for further developments in infectious disease modelling. incidence is available from https://www.repidemicsconsortium.org/incidence.

Evidence for Mother-to-Child Transmission of Zika Virus Through Breast Milk

Zikavirus (ZIKV) is an emerging viral pathogen that continues to spread throughout different regions of the world. Herein we report a case that provides further evidence that ZIKV transmission can occur through breastfeeding by providing a detailed clinical, genomic, and virological case-based description.

Resource-driven encounters among consumers and implications for the spread of infectious disease

Animals share a variety of common resources, which can be a major driver of conspecific encounter rates. In this work, we implement a spatially explicit mathematical model for resource visitation behaviour in order to examine how changes in resource availability can influence the rate of encounters among consumers. Using simulations and asymptotic analysis, we demonstrate that, under a reasonable set of assumptions, the relationship between resource availability and consumer conspecific encounters is not monotonic. We characterize how the maximum encounter rate and associated critical resource density depend on system parameters like consumer density and the maximum distance from which consumers can detect and respond to resources. The assumptions underlying our theoretical model and analysis are motivated by observations of large aggregations of black-backed jackals at carcasses generated by seasonal outbreaks of anthrax among herbivores in Etosha National Park, Namibia. As non-obligate scavengers, black-backed jackals use carcasses as a supplemental food resource when they are available. While jackals do not appear to acquire disease from ingesting anthrax carcasses, changes in their movement patterns in response to changes in carcass abundance do alter jackals' conspecific encounter rate in ways that may affect the transmission dynamics of other diseases, such as rabies. Our theoretical results provide a method to quantify and analyse the hypothesis that the outbreak of a fatal disease among herbivores can potentially facilitate outbreaks of an entirely different disease among jackals. By analysing carcass visitation data, we find support for our model's prediction that the number of conspecific encounters at resource sites decreases with additional increases in resource availability. Whether or not this site-dependent effect translates to an overall decrease in encounters depends, unexpectedly, on the relationship between the maximum distance of detection and the resource density.

Understanding and acting on the developmental origins of health and disease in Africa would improve health across generations

Data from many high- and low- or middle-income countries have linked exposures during key developmental periods (in particular pregnancy and infancy) to later health and disease. Africa faces substantial challenges with persisting infectious disease and now burgeoning non-communicable disease.This paper opens the debate to the value of strengthening the developmental origins of health and disease (DOHaD) research focus in Africa to tackle critical public health challenges across the life-course. We argue that the application of DOHaD science in Africa to advance life-course prevention programmes can aid the achievement of the Sustainable Development Goals, and assist in improving health across generations. To increase DOHaD research and its application in Africa, we need to mobilise multisectoral partners, utilise existing data and expertise on the continent, and foster a new generation of young African scientists engrossed in DOHaD.

Projected Impact of Dengue Vaccination in Yucatán, Mexico

Dengue vaccines will soon provide a new tool for reducing dengue disease, but the effectiveness of widespread vaccination campaigns has not yet been determined. We developed an agent-based dengue model representing movement of and transmission dynamics among people and mosquitoes in Yucatán, Mexico, and simulated various vaccine scenarios to evaluate effectiveness under those conditions. This model includes detailed spatial representation of the Yucatán population, including the location and movement of 1.8 million people between 375,000 households and 100,000 workplaces and schools. Where possible, we designed the model to use data sources with international coverage, to simplify re-parameterization for other regions. The simulation and analysis integrate 35 years of mild and severe case data (including dengue serotype when available), results of a seroprevalence survey, satellite imagery, and climatological, census, and economic data. To fit model parameters that are not directly informed by available data, such as disease reporting rates and dengue transmission parameters, we developed a parameter estimation toolkit called AbcSmc, which we have made publicly available. After fitting the simulation model to dengue case data, we forecasted transmission and assessed the relative effectiveness of several vaccination strategies over a 20 year period. Vaccine efficacy is based on phase III trial results for the Sanofi-Pasteur vaccine, Dengvaxia. We consider routine vaccination of 2, 9, or 16 year-olds, with and without a one-time catch-up campaign to age 30. Because the durability of Dengvaxia is not yet established, we consider hypothetical vaccines that confer either durable or waning immunity, and we evaluate the use of booster doses to counter waning. We find that plausible vaccination scenarios with a durable vaccine reduce annual dengue incidence by as much as 80% within five years. However, if vaccine efficacy wanes after administration, we find that there can be years with larger epidemics than would occur without any vaccination, and that vaccine booster doses are necessary to prevent this outcome.

Dengue is a mosquito-transmitted viral disease that is common throughout the tropics. Despite a long history in humans and extensive efforts to control dengue transmission in many countries, the number, severity, and geographic range of reported cases is increasing. Most control efforts have focused on controlling mosquito populations, but the main vector, Aedes aegypti, flourishes in human-disturbed and indoor environments. Because the mosquitoes prefer to bite during the day when people are active and potentially moving around high-risk locations, fixed barriers like bed nets are not effective. Several dengue vaccines are being actively developed and may become valuable tools in dengue control. Using historical dengue data from Yucatán, Mexico, we fit a detailed simulation of human and mosquito populations to project future transmission, then used efficacy data from vaccine trials to evaluate the benefit of potential vaccination deployment strategies in the region. For a durable vaccine, we find that population-level, annual vaccine effectiveness approaches 65% by the end of the 20-year forecast period. For waning vaccines, however, effectiveness is greatly reduced–and sometimes negative–unless booster vaccinations are used.

Sampling Design Influences the Observed Dominance of Culex tritaeniorhynchus: Considerations for Future Studies of Japanese Encephalitis Virus Transmission

Mosquito sampling during Japanese encephalitis virus (JEV)-associated studies, particularly in India, has usually been conducted via aspirators or light traps to catch mosquitoes around cattle, which are dead-end hosts for JEV. High numbers of Culex tritaeniorhynchus, relative to other species, have often been caught during these studies. Less frequently, studies have involved sampling outdoor resting mosquitoes. We aimed to compare the relative abundance of mosquito species between these two previously used mosquito sampling methods. From September to December 2013 entomological surveys were undertaken in eight villages in a Japanese encephalitis (JE) endemic area of Bangladesh. Light traps were used to collect active mosquitoes in households, and resting boxes and a Bina Pani Das hop cage were used near oviposition sites to collect resting mosquitoes. Numbers of humans and domestic animals present in households where light traps were set were recorded. In five villages Cx. tritaeniorhynchus was more likely to be selected from light trap samples near hosts than resting collection samples near oviposition sites, according to log odds ratio tests. The opposite was true for Cx. pseudovishnui and Armigeres subalbatus, which can also transmit JEV. Culex tritaeniorhynchus constituted 59% of the mosquitoes sampled from households with cattle, 28% from households without cattle and 17% in resting collections. In contrast Cx. pseudovishnui constituted 5.4% of the sample from households with cattle, 16% from households with no cattle and 27% from resting collections, while Ar. subalbatus constituted 0.15%, 0.38%, and 8.4% of these samples respectively. These observations may be due to differences in timing of biting activity, host preference and host-seeking strategy rather than differences in population density. We suggest that future studies aiming to implicate vector species in transmission of JEV should consider focusing catches around hosts able to transmit JEV.

The relative numbers of individuals of each mosquito species in an area are important to estimate when identifying species that contribute the most to vector-borne pathogen transmission. However, methods to sample mosquitoes and enumerate the number of individuals collected often vary in their catch efficacy between species. For example, species that take a bloodmeal during daylight hours are less likely to be caught using a light trap than a species that feeds predominantly at night. Similarly, sampling near a mammalian host will more likely collect mosquitoes with a preference for mammals than those with a preference for birds. In this study we compare sampling methods for assessing the relative abundance of mosquito species that may be involved in Japanese encephalitis virus (JEV) transmission. Collections near cattle- a species unable to transmit JEV- have been influential in implicating Cx. tritaeniorhynchus as the primary vector of JEV in South Asia, due to the high number of individuals of this species caught relative to other species. Indeed, this mosquito constituted the majority of the mosquitoes collected by light traps in households with cattle in this study. However, other species were more common when sampling households without cattle or resting mosquitoes near oviposition sites. We propose that methods used to sample mosquitoes in studies aiming to implicate species in JEV transmission in South Asia be reconsidered given that there are other mosquito species that are able to transmit JEV, and these species may be underrepresented when sampling using light traps near cattle.

Environment, host, and fungal traits predict continental-scale white-nose syndrome in bats

White-nose syndrome is a fungal disease killing bats in eastern North America, but disease is not seen in European bats and is less severe in some North American species. We show that how bats use energy during hibernation and fungal growth rates under different environmental conditions can explain how some bats are able to survive winter with infection and others are not. Our study shows how simple but nonlinear interactions between fungal growth and bat energetics result in decreased survival times at more humid hibernation sites; however, differences between species such as body size and metabolic rates determine the impact of fungal infection on bat survival, allowing European bat species to survive, whereas North American species can experience dramatic decline.

Statistical power and validity of Ebola vaccine trials in Sierra Leone: a simulation study of trial design and analysis

BACKGROUND

Safe and effective vaccines could help to end the ongoing Ebola virus disease epidemic in parts of west Africa, and mitigate future outbreaks of the virus. We assess the statistical validity and power of randomised controlled trial (RCT) and stepped-wedge cluster trial (SWCT) designs in Sierra Leone, where the incidence of Ebola virus disease is spatiotemporally heterogeneous, and is decreasing rapidly.

METHODS

We projected district-level Ebola virus disease incidence for the next 6 months, using a stochastic model fitted to data from Sierra Leone. We then simulated RCT and SWCT designs in trial populations comprising geographically distinct clusters at high risk, taking into account realistic logistical constraints, and both individual-level and cluster-level variations in risk. We assessed false-positive rates and power for parametric and non-parametric analyses of simulated trial data, across a range of vaccine efficacies and trial start dates.

FINDINGS

For an SWCT, regional variation in Ebola virus disease incidence trends produced increased false-positive rates (up to 0·15 at α=0·05) under standard statistical models, but not when analysed by a permutation test, whereas analyses of RCTs remained statistically valid under all models. With the assumption of a 6-month trial starting on Feb 18, 2015, we estimate the power to detect a 90% effective vaccine to be between 49% and 89% for an RCT, and between 6% and 26% for an SWCT, depending on the Ebola virus disease incidence within the trial population. We estimate that a 1-month delay in trial initiation will reduce the power of the RCT by 20% and that of the SWCT by 49%.

INTERPRETATION

Spatiotemporal variation in infection risk undermines the statistical power of the SWCT. This variation also undercuts the SWCT's expected ethical advantages over the RCT, because an RCT, but not an SWCT, can prioritise vaccination of high-risk clusters.

FUNDING

US National Institutes of Health, US National Science Foundation, and Canadian Institutes of Health Research.

Modeling infectious disease dynamics in the complex landscape of global health

Despite some notable successes in the control of infectious diseases, transmissible pathogens still pose an enormous threat to human and animal health. The ecological and evolutionary dynamics of infections play out on a wide range of interconnected temporal, organizational, and spatial scales, which span hours to months, cells to ecosystems, and local to global spread. Moreover, some pathogens are directly transmitted between individuals of a single species, whereas others circulate among multiple hosts, need arthropod vectors, or can survive in environmental reservoirs. Many factors, including increasing antimicrobial resistance, increased human connectivity and changeable human behavior, elevate prevention and control from matters of national policy to international challenge. In the face of this complexity, mathematical models offer valuable tools for synthesizing information to understand epidemiological patterns, and for developing quantitative evidence for decision-making in global health.

The effect of seasonal birth pulses on pathogen persistence in wild mammal populations

The notion of a critical community size (CCS), or population size that is likely to result in long-term persistence of a communicable disease, has been developed based on the empirical observations of acute immunizing infections in human populations, and extended for use in wildlife populations. Seasonal birth pulses are frequently observed in wildlife and are expected to impact infection dynamics, yet their effect on pathogen persistence and CCS have not been considered. To investigate this issue theoretically, we use stochastic epidemiological models to ask how host life-history traits and infection parameters interact to determine pathogen persistence within a closed population. We fit seasonal birth pulse models to data from diverse mammalian species in order to identify realistic parameter ranges. When varying the synchrony of the birth pulse with all other parameters being constant, our model predicted that the CCS can vary by more than two orders of magnitude. Tighter birth pulses tended to drive pathogen extinction by creating large amplitude oscillations in prevalence, especially with high demographic turnover and short infectious periods. Parameters affecting the relative timing of the epidemic and birth pulse peaks determined the intensity and direction of the effect of pre-existing immunity in the population on the pathogen's ability to persist beyond the initial epidemic following its introduction.

School-located influenza vaccination reduces community risk for influenza and influenza-like illness emergency care visits

BACKGROUND

School-located influenza vaccination (SLIV) programs can substantially enhance the sub-optimal coverage achieved under existing delivery strategies. Randomized SLIV trials have shown these programs reduce laboratory-confirmed influenza among both vaccinated and unvaccinated children. This work explores the effectiveness of a SLIV program in reducing the community risk of influenza and influenza-like illness (ILI) associated emergency care visits.

METHODS

For the 2011/12 and 2012/13 influenza seasons, we estimated age-group specific attack rates (AR) for ILI from routine surveillance and census data. Age-group specific SLIV program effectiveness was estimated as one minus the AR ratio for Alachua County versus two comparison regions: the 12 county region surrounding Alachua County, and all non-Alachua counties in Florida.

RESULTS

Vaccination of ∼50% of 5-17 year-olds in Alachua reduced their risk of ILI-associated visits, compared to the rest of Florida, by 79% (95% confidence interval: 70, 85) in 2011/12 and 71% (63, 77) in 2012/13. The greatest indirect effectiveness was observed among 0-4 year-olds, reducing AR by 89% (84, 93) in 2011/12 and 84% (79, 88) in 2012/13. Among all non-school age residents, the estimated indirect effectiveness was 60% (54, 65) and 36% (31, 41) for 2011/12 and 2012/13. The overall effectiveness among all age-groups was 65% (61, 70) and 46% (42, 50) for 2011/12 and 2012/13.

CONCLUSION

Wider implementation of SLIV programs can significantly reduce the influenza-associated public health burden in communities.

Detecting differential transmissibilities that affect the size of self-limited outbreaks

Our ability to respond appropriately to infectious diseases is enhanced by identifying differences in the potential for transmitting infection between individuals. Here, we identify epidemiological traits of self-limited infections (i.e. infections with an effective reproduction number satisfying [0 < R eff < 1) that correlate with transmissibility. Our analysis is based on a branching process model that permits statistical comparison of both the strength and heterogeneity of transmission for two distinct types of cases. Our approach provides insight into a variety of scenarios, including the transmission of Middle East Respiratory Syndrome Coronavirus (MERS-CoV) in the Arabian peninsula, measles in North America, pre-eradication smallpox in Europe, and human monkeypox in the Democratic Republic of the Congo. When applied to chain size data for MERS-CoV transmission before 2014, our method indicates that despite an apparent trend towards improved control, there is not enough statistical evidence to indicate that R eff has declined with time. Meanwhile, chain size data for measles in the United States and Canada reveal statistically significant geographic variation in R eff, suggesting that the timing and coverage of national vaccination programs, as well as contact tracing procedures, may shape the size distribution of observed infection clusters. Infection source data for smallpox suggests that primary cases transmitted more than secondary cases, and provides a quantitative assessment of the effectiveness of control interventions. Human monkeypox, on the other hand, does not show evidence of differential transmission between animals in contact with humans, primary cases, or secondary cases, which assuages the concern that social mixing can amplify transmission by secondary cases. Lastly, we evaluate surveillance requirements for detecting a change in the human-to-human transmission of monkeypox since the cessation of cross-protective smallpox vaccination. Our studies lay the foundation for future investigations regarding how infection source, vaccination status or other putative transmissibility traits may affect self-limited transmission.

Seven challenges for modelling indirect transmission: vector-borne diseases, macroparasites and neglected tropical diseases

Many of the challenges which face modellers of directly transmitted pathogens also arise when modelling the epidemiology of pathogens with indirect transmission – whether through environmental stages, vectors, intermediate hosts or multiple hosts. In particular, understanding the roles of different hosts, how to measure contact and infection patterns, heterogeneities in contact rates, and the dynamics close to elimination are all relevant challenges, regardless of the mode of transmission. However, there remain a number of challenges that are specific and unique to modelling vector-borne diseases and macroparasites. Moreover, many of the neglected tropical diseases which are currently targeted for control and elimination are vector-borne, macroparasitic, or both, and so this article includes challenges which will assist in accelerating the control of these high-burden diseases. Here, we discuss the challenges of indirect measures of infection in humans, whether through vectors or transmission life stages and in estimating the contribution of different host groups to transmission. We also discuss the issues of “evolution-proof” interventions against vector-borne disease.

Recasting the theory of mosquito-borne pathogen transmission dynamics and control

Mosquito-borne diseases pose some of the greatest challenges in public health, especially in tropical and sub-tropical regions of the world. Efforts to control these diseases have been underpinned by a theoretical framework developed for malaria by Ross and Macdonald, including models, metrics for measuring transmission, and theory of control that identifies key vulnerabilities in the transmission cycle. That framework, especially Macdonald's formula for R₀ and its entomological derivative, vectorial capacity, are now used to study dynamics and design interventions for many mosquito-borne diseases. A systematic review of 388 models published between 1970 and 2010 found that the vast majority adopted the Ross–Macdonald assumption of homogeneous transmission in a well-mixed population. Studies comparing models and data question these assumptions and point to the capacity to model heterogeneous, focal transmission as the most important but relatively unexplored component in current theory. Fine-scale heterogeneity causes transmission dynamics to be nonlinear, and poses problems for modeling, epidemiology and measurement. Novel mathematical approaches show how heterogeneity arises from the biology and the landscape on which the processes of mosquito biting and pathogen transmission unfold. Emerging theory focuses attention on the ecological and social context for mosquito blood feeding, the movement of both hosts and mosquitoes, and the relevant spatial scales for measuring transmission and for modeling dynamics and control.

A comparison of bats and rodents as reservoirs of zoonotic viruses: are bats special?

Bats are the natural reservoirs of a number of high-impact viral zoonoses. We present a quantitative analysis to address the hypothesis that bats are unique in their propensity to host zoonotic viruses based on a comparison with rodents, another important host order. We found that bats indeed host more zoonotic viruses per species than rodents, and we identified life-history and ecological factors that promote zoonotic viral richness. More zoonotic viruses are hosted by species whose distributions overlap with a greater number of other species in the same taxonomic order (sympatry). Specifically in bats, there was evidence for increased zoonotic viral richness in species with smaller litters (one young), greater longevity and more litters per year. Furthermore, our results point to a new hypothesis to explain in part why bats host more zoonotic viruses per species: the stronger effect of sympatry in bats and more viruses shared between bat species suggests that interspecific transmission is more prevalent among bats than among rodents. Although bats host more zoonotic viruses per species, the total number of zoonotic viruses identified in bats (61) was lower than in rodents (68), a result of there being approximately twice the number of rodent species as bat species. Therefore, rodents should still be a serious concern as reservoirs of emerging viruses. These findings shed light on disease emergence and perpetuation mechanisms and may help lead to a predictive framework for identifying future emerging infectious virus reservoirs.

A systematic review of mathematical models of mosquito-borne pathogen transmission: 1970-2010

Mathematical models of mosquito-borne pathogen transmission originated in the early twentieth century to provide insights into how to most effectively combat malaria. The foundations of the Ross–Macdonald theory were established by 1970. Since then, there has been a growing interest in reducing the public health burden of mosquito-borne pathogens and an expanding use of models to guide their control. To assess how theory has changed to confront evolving public health challenges, we compiled a bibliography of 325 publications from 1970 through 2010 that included at least one mathematical model of mosquito-borne pathogen transmission and then used a 79-part questionnaire to classify each of 388 associated models according to its biological assumptions. As a composite measure to interpret the multidimensional results of our survey, we assigned a numerical value to each model that measured its similarity to 15 core assumptions of the Ross–Macdonald model. Although the analysis illustrated a growing acknowledgement of geographical, ecological and epidemiological complexities in modelling transmission, most models during the past 40 years closely resemble the Ross–Macdonald model. Modern theory would benefit from an expansion around the concepts of heterogeneous mosquito biting, poorly mixed mosquito-host encounters, spatial heterogeneity and temporal variation in the transmission process.

A comparison of bats and rodents as reservoirs of zoonotic viruses: are bats special?

Bats are the natural reservoirs of a number of high-impact viral zoonoses. We present a quantitative analysis to address the hypothesis that bats are unique in their propensity to host zoonotic viruses based on a comparison with rodents, another important host order. We found that bats indeed host more zoonotic viruses per species than rodents, and we identified life-history and ecological factors that promote zoonotic viral richness. More zoonotic viruses are hosted by species whose distributions overlap with a greater number of other species in the same taxonomic order (sympatry). Specifically in bats, there was evidence for increased zoonotic viral richness in species with smaller litters (one young), greater longevity and more litters per year. Furthermore, our results point to a new hypothesis to explain in part why bats host more zoonotic viruses per species: the stronger effect of sympatry in bats and more viruses shared between bat species suggests that interspecific transmission is more prevalent among bats than among rodents. Although bats host more zoonotic viruses per species, the total number of zoonotic viruses identified in bats (61) was lower than in rodents (68), a result of there being approximately twice the number of rodent species as bat species. Therefore, rodents should still be a serious concern as reservoirs of emerging viruses. These findings shed light on disease emergence and perpetuation mechanisms and may help lead to a predictive framework for identifying future emerging infectious virus reservoirs.

Model-guided fieldwork: practical guidelines for multidisciplinary research on wildlife ecological and epidemiological dynamics

Infectious disease ecology has recently raised its public profile beyond the scientific community due to the major threats that wildlife infections pose to biological conservation, animal welfare, human health and food security. As we start unravelling the full extent of emerging infectious diseases, there is an urgent need to facilitate multidisciplinary research in this area. Even though research in ecology has always had a strong theoretical component, cultural and technical hurdles often hamper direct collaboration between theoreticians and empiricists. Building upon our collective experience of multidisciplinary research and teaching in this area, we propose practical guidelines to help with effective integration among mathematical modelling, fieldwork and laboratory work. Modelling tools can be used at all steps of a field-based research programme, from the formulation of working hypotheses to field study design and data analysis. We illustrate our model-guided fieldwork framework with two case studies we have been conducting on wildlife infectious diseases: plague transmission in prairie dogs and lyssavirus dynamics in American and African bats. These demonstrate that mechanistic models, if properly integrated in research programmes, can provide a framework for holistic approaches to complex biological systems.

The application of one health approaches to henipavirus research

Henipaviruses cause fatal infection in humans and domestic animals. Transmission from fruit bats, the wildlife reservoirs of henipaviruses, is putatively driven (at least in part) by anthropogenic changes that alter host ecology. Human and domestic animal fatalities occur regularly in Asia and Australia, but recent findings suggest henipaviruses are present in bats across the Old World tropics. We review the application of the One Health approach to henipavirus research in three locations: Australia, Malaysia and Bangladesh. We propose that by recognising and addressing the complex interaction among human, domestic animal and wildlife systems, research within the One Health paradigm will be more successful in mitigating future human and domestic animal deaths from henipavirus infection than alternative single-discipline approaches.

Agricultural intensification, priming for persistence and the emergence of Nipah virus: a lethal bat-borne zoonosis

Emerging zoonoses threaten global health, yet the processes by which they emerge are complex and poorly understood. Nipah virus (NiV) is an important threat owing to its broad host and geographical range, high case fatality, potential for human-to-human transmission and lack of effective prevention or therapies. Here, we investigate the origin of the first identified outbreak of NiV encephalitis in Malaysia and Singapore. We analyse data on livestock production from the index site (a commercial pig farm in Malaysia) prior to and during the outbreak, on Malaysian agricultural production, and from surveys of NiV's wildlife reservoir (flying foxes). Our analyses suggest that repeated introduction of NiV from wildlife changed infection dynamics in pigs. Initial viral introduction produced an explosive epizootic that drove itself to extinction but primed the population for enzootic persistence upon reintroduction of the virus. The resultant within-farm persistence permitted regional spread and increased the number of human infections. This study refutes an earlier hypothesis that anomalous El Niño Southern Oscillation-related climatic conditions drove emergence and suggests that priming for persistence drove the emergence of a novel zoonotic pathogen. Thus, we provide empirical evidence for a causative mechanism previously proposed as a precursor to widespread infection with H5N1 avian influenza and other emerging pathogens.