Sean M. Moore

Predicting the effect of climate change on African trypanosomiasis: integrating epidemiology with parasite and vector biology

Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector–parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.7°C and 26.1°C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46–77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change.

Improvement of Disease Prediction and Modeling through the Use of Meteorological Ensembles: Human Plague in Uganda

Climate and weather influence the occurrence, distribution, and incidence of infectious diseases, particularly those caused by vector-borne or zoonotic pathogens. Thus, models based on meteorological data have helped predict when and where human cases are most likely to occur. Such knowledge aids in targeting limited prevention and control resources and may ultimately reduce the burden of diseases. Paradoxically, localities where such models could yield the greatest benefits, such as tropical regions where morbidity and mortality caused by vector-borne diseases is greatest, often lack high-quality in situ local meteorological data. Satellite- and model-based gridded climate datasets can be used to approximate local meteorological conditions in data-sparse regions, however their accuracy varies. Here we investigate how the selection of a particular dataset can influence the outcomes of disease forecasting models. Our model system focuses on plague (Yersinia pestis infection) in the West Nile region of Uganda. The majority of recent human cases have been reported from East Africa and Madagascar, where meteorological observations are sparse and topography yields complex weather patterns. Using an ensemble of meteorological datasets and model-averaging techniques we find that the number of suspected cases in the West Nile region was negatively associated with dry season rainfall (December-February) and positively with rainfall prior to the plague season. We demonstrate that ensembles of available meteorological datasets can be used to quantify climatic uncertainty and minimize its impacts on infectious disease models. These methods are particularly valuable in regions with sparse observational networks and high morbidity and mortality from vector-borne diseases.

Heterogeneity of the HIV epidemic in agrarian, trading, and fishing communities in Rakai, Uganda: an observational epidemiological study

BACKGROUND Understanding the extent to which HIV burden differs across communities and the drivers of local disparities is crucial for an effective and targeted HIV response. We assessed community-level variations in HIV prevalence, risk factors, and treatment and prevention service uptake in Rakai, Uganda. METHODS The Rakai Community Cohort Study (RCCS) is an open, population-based cohort of people aged 15-49 years in 40 communities. Participants are HIV tested and interviewed to obtain sociodemographic, behavioural, and health information. RCCS data from Aug 10, 2011, to May 30, 2013, were used to classify communities as agrarian (n=27), trading (n=9), or lakeside fishing sites (n=4). We mapped HIV prevalence with Bayesian methods, and characterised variability across and within community classifications. We also assessed differences in HIV risk factors and uptake of antiretroviral therapy and male circumcision between community types. FINDINGS 17 119 individuals were included, 9215 (54%) of whom were female. 9931 participants resided in agrarian, 3318 in trading, and 3870 in fishing communities. Median HIV prevalence was higher in fishing communities (42%, range 38-43) than in trading (17%, 11-21) and agrarian communities (14%, 9-26). Antiretroviral therapy use was significantly lower in both men and women in fishing communities than in trading (age-adjusted prevalence risk ratio in men 0·64, 95% CI 0·44-0·97; women 0·53, 0·42-0·66) and agrarian communities (men 0·55, 0·42-0·72; women 0·65, 0·54-0·79), as was circumcision coverage among men (vs trading 0·48, 0·42-0·55; vs agrarian 0·64, 0·56-0·72). Self-reported risk behaviours were significantly higher in men than in women and in fishing communities than in other community types. INTERPRETATION Substantial heterogeneity in HIV prevalence, risk factors, and service uptake in Rakai, Uganda, emphasises the need for local surveillance and the design of targeted HIV responses. High HIV burden, risk behaviours, and low use of combination HIV prevention in fishing communities make these populations a priority for intervention. FUNDING National Institute of Mental Health, the National Institute of Allergy and Infectious Diseases, the National Institute of Child Health and Development, and the National Institute for Allergy and Infectious Diseases Division of Intramural Research, National Institutes of Health; the Bill & Melinda Gates Foundation; and the Johns Hopkins University Center for AIDS Research.Summary Background Understanding the extent to which HIV burden differs across communities and the drivers of local disparities is critical for an effective and targeted HIV response. We assessed community-level variations in HIV prevalence, risk factors, and treatment and prevention service uptake in Rakai, Uganda. Methods The Rakai Community Cohort Study (RCCS) is an open, population-based cohort surveying persons aged 15–49 in 40 communities. Participants are HIV tested and interviewed to obtain sociodemographic, behavioral, and health information. RCCS data from August 2011 to May 2013 were used to classify communities as agrarian (n=27), trading (n=9), or lakeside fishing sites (n=4). HIV prevalence was mapped using Bayesian methods, and variability across and within community classifications was characterized. Differences in HIV risk factors and uptake of antiretroviral therapy and male circumcision between community types were assessed. Findings 17,119 individuals were included; 9215 (54%) were female. 9931 participants resided in agrarian, 3318 in trading, and 3870 in fishing communities. There was large variation in HIV prevalence, ranging from 9% to 43% across communities. Fishing communities had a higher median HIV prevalence (41%, range: 37–43%) compared to trading (17%, range: 11–22%) and agrarian communities (14%, range: 9–26%); ART and male circumcision coverage were significantly lower in fishing communities. Self-reported risk behaviors were significantly higher in men compared to women and in fishing communities compared to other community types. Interpretation There is substantial heterogeneity in HIV prevalence, risk factors, and service uptake across communities within one region of Uganda. These findings underscore the need for local surveillance and have important implications for the design of targeted HIV responses. In particular, the extremely high HIV burden and risk behaviors, and low use of combination HIV prevention in fishing communities make these areas a priority for intervention.

The influence of host diversity and composition on epidemiological patterns at multiple spatial scales

Spatial patterns of pathogen prevalence are determined by ecological processes acting across multiple spatial scales. Host-pathogen interactions are influenced by community composition, landscape structure, and environmental factors. Explaining prevalence patterns requires an understanding of how local determinants of infection, such as community composition, are mediated by landscape characteristics and regional-scale environmental drivers. Here we investigate the role of local community interactions and the effects of landscape structure on the dynamics of barley and cereal yellow dwarf viruses (B/CYDV) in the open meadows of the Cascade Mountains of Oregon. B/CYDV is an aphid-transmitted, generalist pathogen of over 100 wild and cultivated grass species. We used variance components analysis and model selection techniques to partition the sources of variation in B/CYDV prevalence and to determine which abiotic and biotic factors influence host-pathogen interactions in a Cascades meadowsystem. B/CYDV prevalence in Cascades meadows varied by host species identity, with a significantly higher proportion of infected Festuca idahoensis individuals than Elymus glaucus or Bromus carinatus. Although there was significant variation in prevalence among host species and among meadows in the same meadow complex, there was no evidence of any significant variation in prevalence among different meadow complexes at a larger spatial scale. Variation in prevalence among meadows was primarily associated with the local community context (host identity, the relative abundance of different host species, and host species richness) and the physical landscape attributes of the meadow. These results highlight the importance of local host community composition, mediated by landscape characteristics such as meadow aspect, as a determinant of the spatial pattern of infection of a multi-host pathogen.

Unraveling the drivers of MERS-CoV transmission

Significance Since it was discovered in 2012, Middle East respiratory syndrome coronavirus (MERS-CoV) has infected more than 1,700 persons, one-third of whom died, essentially in the Middle East. Persons can get infected by direct or indirect contact with dromedary camels, and although human-to-human transmission is not self-sustaining in the Middle East, it can nonetheless generate large outbreaks, particular in hospital settings. Overall, we still poorly understand how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics. Here, we quantify the contribution of each of these factors from detailed records of MERS-CoV cases from the Kingdom of Saudi Arabia, which has been the most affected country. With more than 1,700 laboratory-confirmed infections, Middle East respiratory syndrome coronavirus (MERS-CoV) remains a significant threat for public health. However, the lack of detailed data on modes of transmission from the animal reservoir and between humans means that the drivers of MERS-CoV epidemics remain poorly characterized. Here, we develop a statistical framework to provide a comprehensive analysis of the transmission patterns underlying the 681 MERS-CoV cases detected in the Kingdom of Saudi Arabia (KSA) between January 2013 and July 2014. We assess how infections from the animal reservoir, the different levels of mixing, and heterogeneities in transmission have contributed to the buildup of MERS-CoV epidemics in KSA. We estimate that 12% [95% credible interval (CI): 9%, 15%] of cases were infected from the reservoir, the rest via human-to-human transmission in clusters (60%; CI: 57%, 63%), within (23%; CI: 20%, 27%), or between (5%; CI: 2%, 8%) regions. The reproduction number at the start of a cluster was 0.45 (CI: 0.33, 0.58) on average, but with large SD (0.53; CI: 0.35, 0.78). It was >1 in 12% (CI: 6%, 18%) of clusters but fell by approximately one-half (47% CI: 34%, 63%) its original value after 10 cases on average. The ongoing exposure of humans to MERS-CoV from the reservoir is of major concern, given the continued risk of substantial outbreaks in health care systems. The approach we present allows the study of infectious disease transmission when data linking cases to each other remain limited and uncertain.

Climate Predictors of the Spatial Distribution of Human Plague Cases in the West Nile Region of Uganda

East Africa has been identified as a region where vector-borne and zoonotic diseases are most likely to emerge or re-emerge and where morbidity and mortality from these diseases is significant. Understanding when and where humans are most likely to be exposed to vector-borne and zoonotic disease agents in this region can aid in targeting limited prevention and control resources. Often, spatial and temporal distributions of vectors and vector-borne disease agents are predictable based on climatic variables. However, because of coarse meteorological observation networks, appropriately scaled and accurate climate data are often lacking for Africa. Here, we use a recently developed 10-year gridded meteorological dataset from the Advanced Weather Research and Forecasting Model to identify climatic variables predictive of the spatial distribution of human plague cases in the West Nile region of Uganda. Our logistic regression model revealed that within high elevation sites (above 1,300 m), plague risk was positively associated with rainfall during the months of February, October, and November and negatively associated with rainfall during the month of June. These findings suggest that areas that receive increased but not continuous rainfall provide ecologically conducive conditions for Yersinia pestis transmission in this region. This study serves as a foundation for similar modeling efforts of other vector-borne and zoonotic disease in regions with sparse observational meteorologic networks.

Meteorological Influences on the Seasonality of Lyme Disease in the United States

Lyme disease (Borrelia burgdorferi infection) is the most common vector-transmitted disease in the United States. The majority of human Lyme disease (LD) cases occur in the summer months, but the timing of the peak occurrence varies geographically and from year to year. We calculated the beginning, peak, end, and duration of the main LD season in 12 highly endemic states from 1992 to 2007 and then examined the association between the timing of these seasonal variables and several meteorological variables. An earlier beginning to the LD season was positively associated with higher cumulative growing degree days through Week 20, lower cumulative precipitation, a lower saturation deficit, and proximity to the Atlantic coast. The timing of the peak and duration of the LD season were also associated with cumulative growing degree days, saturation deficit, and cumulative precipitation, but no meteorological predictors adequately explained the timing of the end of the LD season.

A Regional Climatography of West Nile, Uganda, to Support Human Plague Modeling

AbstractThe West Nile region in northwestern Uganda is a focal point for human plague, which peaks in boreal autumn and is spread by fleas that travel on rodent hosts. The U.S. Centers for Disease Control and Prevention is collaborating with the National Center for Atmospheric Research to quantitatively address the linkages between climate and human plague in this region. The aim of this paper is to advance knowledge of the climatic conditions required to maintain enzootic cycles and to trigger epizootic cycles and ultimately to target limited surveillance, prevention, and control resources. A hybrid dynamical–statistical downscaling technique was applied to simulations from the Weather Research and Forecasting Model (WRF) to generate a multiyear 2-km climate dataset for modeling plague in the West Nile region. The resulting dataset resolves the spatial variability and annual cycle of temperature, humidity, and rainfall in West Nile relative to satellite-based and in situ records. Topography exerts a firs...

Climate change influences on the annual onset of Lyme disease in the United States

Lyme disease is the most commonly reported vector-borne illness in the United States. Lyme disease occurrence is highly seasonal and the annual springtime onset of cases is modulated by meteorological conditions in preceding months. A meteorological-based empirical model for Lyme disease onset week in the United States is driven with downscaled simulations from five global climate models and four greenhouse gas emissions scenarios to project the impacts of 21st century climate change on the annual onset week of Lyme disease. Projections are made individually and collectively for the 12 eastern States where >90% of cases occur. The national average annual onset week of Lyme disease is projected to become 0.4-0.5 weeks earlier for 2025-2040 (p<0.05), and 0.7-1.9 weeks earlier for 2065-2080 (p<0.01), with the largest shifts for scenarios with the highest greenhouse gas emissions. The more southerly mid-Atlantic States exhibit larger shifts (1.0-3.5 weeks) compared to the Northeastern and upper Midwestern States (0.2-2.3 weeks) by 2065-2080. Winter and spring temperature increases primarily cause the earlier onset. Greater spring precipitation and changes in humidity partially counteract the temperature effects. The model does not account for the possibility that abrupt shifts in the life cycle of Ixodes scapularis, the primary vector of the Lyme disease spirochete Borrelia burgdorferi in the eastern United States, may alter the disease transmission cycle in unforeseen ways. The results suggest 21st century climate change will make environmental conditions suitable for earlier annual onset of Lyme disease cases in the United States with possible implications for the timing of public health interventions.

Epidemic risk from cholera introductions into Mexico.

Stemming from the 2010 cholera outbreak in Haiti, cholera transmission in Hispaniola continues with over 40,000 cases in 2013. The presence of an ongoing cholera outbreak in the region poses substantial risks to countries throughout the Americas, particularly in areas with poor infrastructure. Since September 9, 2013 nearly 200 cholera cases have been reported in Mexico, as a result of introductions from Hispaniola or Cuba. There appear to have been multiple introductions into Mexico resulting in outbreaks of 2 to over 150 people. Using publicly available data, we attempt to estimate the reproductive number (R) of cholera in Mexico, and thereby assess the potential of continued introductions to establish a sustained epidemic. We estimate R for cholera in Mexico to be between 0.8 to 1.1, depending on the number of introductions, with the confidence intervals for the most plausible estimates crossing 1. These results suggest that the efficiency of cholera transmission in some regions of Mexico is near that necessary for a large epidemic. Intensive surveillance, evaluation of water and sanitation infrastructure, and planning for rapid response are warranted steps to avoid potential large epidemics in the region.