Biology subjects

Lessler, J.

Publications and source records attributed to Lessler, J..

11 recordsLinked to original sources

bioRxiv2026

Crop-specific agricultural land use and Rift Valley fever outbreaks in Uganda: a longitudinal compositional analysis

Rift Valley fever (RVF) is a mosquito-borne disease that can cause severe illness and death in both humans and livestock. Since 2016, Uganda has experienced recurrent but localized RVF outbreaks concentrated in the countrys southwestern region. The ecological drivers of this emergence remain unclear, as outbreaks have occurred throughout the year and show little association with meteorological patterns. We evaluated whether crop cultivation, particularly banana cultivation, is associated with RVF outbreak occurrence after controlling for likely confounders. We conducted a longitudinal study of human-inhabited 5 x 5 km grid cells across southwestern Uganda from 2016-2024. Annual Sentinel-2 satellite imagery composites were used to classify land cover into banana, coffee, ground crops, and non-crop categories, and the proportion of each land type was calculated for every grid-cell year. Because land cover proportions are compositional, isometric log-ratio transformations were used to estimate the independent effects of each land type. Confounding was addressed through propensity weighting, and crop substitution effects were estimated using g-computation. Banana land cover was the only land type consistently associated with increased RVF outbreak likelihood. In grid-cell years with low baseline banana cover, a 10-percentage point substitution from other land classes into banana was associated with a 1.64-fold increase in the odds of an RVF outbreak (95% CI: 1.17-2.29). In a simplified banana-only model, each 10-percentage point increase in banana cover was associated with a 1.21-fold increase in outbreak odds (95% CI: 1.02-1.43). Holding banana cover constant, substitutions among coffee, ground crop, and non-crop land showed weak or null associations. These findings suggest that banana cultivation may be an important ecological feature influencing RVF transmission dynamics and outbreak risk in southwestern Uganda. Author SummaryRift Valley fever (RVF) is a mosquito-borne disease that affects both humans and livestock and has caused repeated outbreaks in southwestern Uganda since 2016. While rainfall and flooding are often linked to RVF outbreaks elsewhere, Ugandas recent outbreaks have occurred across seasons and are not well explained by weather patterns alone. We investigated whether agricultural land use could help explain where outbreaks occur. Using satellite imagery from 2016-2024, we measured the amount of banana cultivation, coffee cultivation, ground crops, and non-crop land across southwestern Uganda and evaluated their association with RVF outbreak occurrence. We found that areas with greater banana cultivation were consistently more likely to experience RVF outbreaks, even after accounting for environmental and demographic factors. In contrast, coffee, ground crops, and non-crop land showed little evidence of an independent association with outbreak risk. These findings suggest that banana cultivation may create ecological conditions that favor RVF transmission. Rather than indicating that bananas themselves cause disease, the results point to banana-growing landscapes as potential environments where interactions among mosquitoes, livestock, and humans may increase transmission opportunities. Understanding these local ecological drivers could help improve surveillance, risk assessment, and prevention strategies for RVF in Uganda and other endemic regions.

ecology↗

bioRxiv2021

Periodic synchronization of dengue epidemics in Thailand: the roles played by temperature and immunity

The spatial distribution of dengue and its vectors (spp. Aedes) may be the widest it has ever been, and projections suggest that climate change may allow the expansion to continue. However, the largest impacts of climate change on dengue might be in regions where the pathogen is already endemic. In these areas, the waxing and waning of immunity has a large impact on temporal dynamics of cases of dengue haemorrhagic fever. Here, we use 51 years of data across 72 provinces and characterise spatio-temporal patterns of dengue in Thailand, where dengue has caused almost 1.5 million cases over the last thirty years, and examine the roles played by temperature and dynamics of immunity in giving rise to those patterns. We find that timescales of multiannual oscillations in dengue vary in space and time and uncover an interesting spatial phenomenon: Thailand has experienced multiple, periodic synchronization events. We show that patterns in synchrony of dengue are consistent with those observed in temperature. Applying a temperature-driven dengue model, we explore how dynamics of immunity interact with temperature to produce the observed multiannual dynamics and patterns in synchrony. While multiannual oscillations are readily produced by immunity in absence of multiannual timescales in temperature, synchrony in temperature can synchronise dengue dynamics in different locations. However, at higher mean temperatures and lower seasonal variation, immune dynamics become more predominant, and dengue dynamics become more insensitive to multiannual fluctuations in temperature. These findings can help underpin predictions of disease patterns as global temperatures rise. Author summary

ecology↗

bioRxiv2019

Serosolver: an open source tool to infer epidemiological and immunological dynamics from serological data

We present a flexible, open source R package designed to obtain additional biological and epidemiological insights from commonly available serological datasets. Analysis of serological responses against pathogens with multiple strains such as influenza pose a specific statistical challenge because observed antibody responses measured in serological assays depend both on unobserved prior infections and the resulting cross-reactive antibody dynamics that these infections generate. We provide a general modelling framework to jointly infer these two typically confounded biological processes using antibody titres against current and historical strains. We do this by linking latent infection dynamics with a mechanistic model of antibody dynamics that generates expected antibody titres over time. This makes it possible to use observations of antibodies in serological assays to infer an individuals infection history as well as the parameters of the antibody process model. Our aim is to provide a flexible inference package that can be applied to a range of datasets studying different viruses over different timescales. We present two case studies to illustrate how our model can infer key immunological parameters, such as antibody titre boosting, waning and cross-reaction, and well as latent epidemiological processes such as attack rates and age-stratified infection risk.

immunology↗

bioRxiv2019

The impact of geographic targeting of oral cholera vaccination in sub-Saharan Africa: a modeling study

BackgroundIn May 2018, the World Health Assembly committed to reducing worldwide cholera deaths by 90% by 2030. Oral cholera vaccine (OCV) plays a key role in reducing the near-term risk of cholera, although global supplies are limited. Characterizing the potential impact and cost-effectiveness of mass OCV deployment strategies is critical for setting expectations and developing cholera control plans that maximize chances of success. Methods and FindingsWe compared the projected impacts of vaccination campaigns across sub-Saharan Africa from 2018 through 2030 when targeting geographically according to historical cholera burden and risk factors. We assessed the number of averted cases, deaths, disability-adjusted life-years, and cost-effectiveness with models that account for direct and indirect vaccine effects and population projections over time. Under current vaccine supply projections, an approach optimized to targeting by historical burden is projected to avert 828,971 (95% CI: 803,370-859,980) cases (equivalent to 34.0% of projected cases; 95% CI: 33.2-34.8). An approach that balances logistical feasibility with targeting historical burden is projected to avert 617,424 (95% CI: 599,150-643,891) cases. In contrast, approaches optimized for targeting locations with limited access to water and sanitation are projected to avert 273,939 (95% CI: 270,319-277,002) and 109,817 (95% CI: 103,735-114,110) cases, respectively. We find that the most logistically feasible targeting strategy costs $1,843 (95% CI: 1,328-14,312) per DALY averted during this period and that effective geographic targeting of OCV campaigns can have a greater impact on cost-effectiveness than improvements to vaccine efficacy and moderate increases in coverage. Although our modeling approach did not project annual changes in baseline cholera risk or incorporate immunity from natural cholera infection, our estimates of the relative performance of different vaccination strategies should be robust to these factors. ConclusionsOur study suggests that geographic targeting is critical to the cost-effectiveness and impact of oral cholera vaccination campaigns. Districts with the poorest access to improved water and sanitation are not the same as districts with the greatest historical cholera incidence. While OCV campaigns can improve cholera control in the near-term, without rapid progress in developing water and sanitation services, our results suggest that vaccine use alone are unlikely to allow us to achieve the 2030 goals.

epidemiology↗

bioRxiv2019

Dengue after Zika: characterizing impacts of Zika emergence on endemic dengue transmission

In 2015 and 2016, Zika virus (ZIKV) swept through many Latin American countries where dengue virus (DENV) is endemic. Dengue and Zika viruses are of the same family, share a vector and may interact competitively or synergistically through human immune responses. We examine dengue incidence data from Brazil and Colombia from before, during, and after the Zika epidemic. We find evidence that dengue incidence was atypically low in 2017 in both Brazil and Colombia. We investigate whether Zika incidence at the state or department level is associated with changes in dengue incidence and find mixed results. We use simulations to investigate expected impact of cross-protection or enhancement between dengue and Zika. Our simulations show that regardless of the mechanism, low periods of dengue incidence are followed by a resurgence in dengue cases. It is therefore likely that countries currently experiencing low levels of dengue incidence will experience large dengue seasons in the near future. By considering multiple combinations of DENV and ZIKV reproduction numbers, we demonstrate that the mixed results of our statistical models are not entirely unexpected. Correlations in DENV and ZIKV reproduction number could contribute to complicating or masking an association between their case counts.

epidemiology↗

bioRxiv2018

Perfect Counterfactuals for Epidemic Simulations

Simulation studies are often used to predict the expected impact of control measures in infectious disease outbreaks. Typically, two independent sets of simulations are conducted, one with the intervetnion, and one without, and epidemic sizes (or some related metric) are compared to estimate the effect of the intervention. Since it is possible that controlled epidemics are larger than uncontrolled ones if there is substantial stochastic variation between epidemics, uncertainty intervals from this approach can include a negative effect even for an effective intervention. To more precisely estimate the number of cases an intervention will prevent within a single epidemic, here we develop a single world approach to matching simulations of controlled epidemics to their exact uncontrolled counterfac-tual. Our method borrows concepts from percolation approaches prune out possible epidemic histories and create potential epidemic graph that can be realized to create perfectly matched controlled and uncontrolled epidemics. We present an implementation of this method for a common class of compartmental models, and its application in a simple SIR model. Results illustrate how, at the cost of some computation time, this method substantially narrows confidence intervals and avoids non-sensical inferences.

epidemiology↗

bioRxiv2018

Differential human mobility and local variation in human infection attack rate

Infectious disease transmission in animals is an inherently spatial process in which a hosts home location and their social mixing patterns are important, with the mixing of infectious individuals often different to that of susceptible individuals. Although incidence data for humans have traditionally been aggregated into low-resolution data sets, modern representative surveillance systems such as electronic hospital records generate high volume case data with precise home locations. Here, we use a high resolution gridded spatial transmission model of arbitrary resolution to investigate the theoretical relationship between population density, differential population movement and local variability in incidence. We show analytically that uniform local attack rate is only possible for individual pixels in the grid if susceptible and infectious individuals move in the same way. Using a population in Guangdong, China, for which a robust quantitative description of movement is available (a movement kernel), and a natural history consistent with pandemic influenza; we show that for the estimated kernel, local cumulative incidence is positively correlated with population density when susceptible individuals are more connected in space than infectious individuals. Conversely, when infectious individuals are more connected, local cumulative incidence is negatively correlated with population density. The amplitude of correlation is substantial for the estimated kernel. However, the strength and direction of correlation changes sign for other kernel parameter values. These results describe a precise relationship between the spatio-social mixing of infectious and susceptible individuals and local variability in attack rates, and suggest a plausible mechanism for the counter-intuitive scenario in which local incidence is lower on average in less dense populations. Also, these results suggest that if spatial transmission models are implemented at high resolution to investigate local disease dynamics, including micro-tuning of interventions, the underlying detailed assumptions about the mechanisms of transmission become more important than when similar studies are conducted at larger spatial scales.\n\nAuthor SummaryWe know that some places have higher rates of infectious disease than others. However, at the moment, we usually only measure these differences for large towns and cities. With modern data, such as those we can get from mobile phones, we can measure rates of infection at much smaller scales. In this paper, we used a computer simulation of an epidemic to propose ways that rates of incidence in small local areas might be related to population density. We found that if infectious people are better connected than non-infectious people, perhaps because they receive visitors, then, on average, higher density areas would have lower rates of infection. If infectious people were less connected than non-infectious people then higher density areas would have higher rates of infection. As data get more accurate, this type of analysis will allow us to propose and test ways to optimize interventions such as the delivery of vaccines and antivirals during a pandemic.

epidemiology↗

bioRxiv2018

Micro-hotspots of Risk in Urban Cholera Epidemics

Targeted interventions have been delivered to neighbors of cholera cases in epidemic responses in Haiti and Africa despite little evidence supporting impact. Using data from urban epidemics in Chad and D.R. Congo we estimate the size and extent of spatiotemporal zones of increased cholera risk around cases. In both cities, we found zones of increased risk of at least 200-meters during the 5-days immediately following case presentation to a clinic. Risk was highest for those living closest to cases and diminished in time and space similarly across settings. These results provide a rational basis for targeting interventions, if delivered rapidly.

epidemiology↗

bioRxiv2017

Simulations for Designing and Interpreting Intervention Trials in Infectious Diseases

Here we urge the adoption of a new paradigm for the design and interpretation of intervention trials in infectious diseases, particularly in emerging infectious disease, that more accurately reflects the dynamics of the transmission process. Interventions in infectious diseases can have indirect effects on those not receiving the intervention as well as direct effects on those receiving the intervention. Combinations of interventions can have complex interactions at the population level. These often cannot be adequately addressed with standard study designs and analytic methods. Simulations can help to accurately represent transmission dynamics in an increasingly complex world which is critical for proper trial design and interpretation. Some ethical aspects of a trial can also be quantified using simulations. After a trial has been conducted, simulations can be used to explore possible explanations for the observed effects. A great deal is to be gained through a multidisciplinary approach that builds collaborations among experts in infectious disease dynamics, epidemiology, statistical science, economics, simulation methods and the conduct of clinical trials.

epidemiology↗

bioRxiv2017

Preliminary results of models to predict areas in the Americas with increased likelihood of Zika virus transmission in 2017.

Numerous Zika virus vaccines are being developed. However, identifying sites to evaluate the efficacy of a Zika virus vaccine is challenging due to the general decrease in Zika virus activity. We compare results from three different modeling approaches to estimate areas that may have increased relative risk of Zika virus transmission during 2017. The analysis focused on eight priority countries (i.e., Brazil, Colombia, Costa Rica, Dominican Republic, Ecuador, Mexico, Panama, and Peru). The models projected low incidence rates during 2017 for all locations in the priority countries but identified several subnational areas that may have increased relative risk of Zika virus transmission in 2017. Given the projected low incidence of disease, the total number of participants, number of study sites, or duration of study follow-up may need to be increased to meet the efficacy study endpoints.

epidemiology↗

bioRxiv2017

Quantifying the Risk and Cost of Active Monitoring for Infectious Diseases

During outbreaks of deadly emerging pathogens (e.g., Ebola, MERS-CoV) and bioterror threats (e.g., smallpox), actively monitoring potentially infected individuals aims to limit disease transmission and morbidity. Guidance issued by CDC on active monitoring was a cornerstone of its response to the West Africa Ebola outbreak. There are limited data on how to balance the costs and performance of this important public health activity. We present a framework that estimates the risks and costs of specific durations of active monitoring for pathogens of significant public health concern. We analyze data from New York Citys Ebola active monitoring program over a 16-month period in 2014-2016. For monitored individuals, we identified unique durations of active monitoring that minimize expected costs for those at \"low (but not zero) risk\" and \"some or high risk\": 21 and 31 days, respectively. Extending our analysis to smallpox and MERS-CoV, we found that the optimal length of active monitoring relative to the median incubation period was reduced compared to Ebola due to less variable incubation periods. Active monitoring can save lives but is expensive. Resources can be most effectively allocated by using exposure-risk categories to modify the duration or intensity of active monitoring.

epidemiology↗