Susceptible host availability modulates climate effects on dengue dynamics

Experiments and models suggest that climate affects mosquito-borne disease transmission. However, disease transmission involves complex nonlinear interactions between climate and population dynamics, which makes detecting climate drivers at the population level challenging. By analysing incidence data, estimated susceptible population size, and climate data with methods based on nonlinear time series analysis (collectively referred to as empirical dynamic modelling), we identified drivers and their interactive effects on dengue dynamics in San Juan, Puerto Rico. Climatic forcing arose only when susceptible availability was high: temperature and rainfall had net positive and negative effects respectively. By capturing mechanistic, nonlinear and context-dependent effects of population susceptibility, temperature and rainfall on dengue transmission empirically, our model improves forecast skill over recent, state-of-the-art models for dengue incidence. Together, these results provide empirical evidence that the interdependence of host population susceptibility and climate drives dengue dynamics in a nonlinear and complex, yet predictable way.R35GM133439 - NIH HHS; DBI-1667584 - National Science Foundation; DEB-1655203 - National Science Foundation; 00028335 - Lenfest Foundation; Stanford University: Bing Fellowship in Honor of Paul Ehrlich, Stanford Data Science Scholars program, Lindsay Family E-IPER Fellowship, Illich-Sadowsky Interdisciplinary Graduate Fellowship, Terman Fellowship, King Center for Global Development seed grant; SERDP 15 RC-2509 - U.S. Department of Defense; University of California San Diego: McQuown Chair in Natural Sciences; DBI-1611767 - National Science Foundation; RAPID DEB-1640780 - National Science Foundation; R35GM133439 - NIH HHS; Hellman Foundation: Hellman Faculty Fellowship; Stanford Woods Institute for the Environment: Environmental Ventures Program; DEB-1518681 - National Science Foundation; R35 GM133439 - NIGMS NIH HHS; DEB-2011147 - National Science Foundationhttps://www.biorxiv.org/content/biorxiv/early/2020/10/19/2019.12.20.883363.full.pdfAccepted manuscrip

Global malaria predictors at a localized scale

Malaria is a life-threatening disease caused by Plasmodium parasites transmitted by Anopheles mosquitoes. In 2022, more than 249 million cases of malaria were reported worldwide, with an estimated 608,000 deaths. While malaria incidence has decreased globally in recent decades, some public health gains have plateaued, and many endemic hotspots still face high transmission rates. Understanding local drivers of malaria transmission is crucial but challenging due to the complex interactions between climate, entomological and human variables, and land use. This study focuses on highly climatically suitable and endemic areas in Côte d’Ivoire to assess the explanatory power of coarse climatic predictors of malaria transmission at a fine scale. Using data from 40 villages participating in a randomized controlled trial of a household malaria intervention, the study examines the effects of climate variation over time on malaria transmission. Through panel regressions and statistical modeling, the study investigates which variable (temperature, precipitation, or entomological inoculation rate) and its form (linear or unimodal) best explains seasonal malaria transmission and the factors predicting spatial variation in transmission. The results highlight the importance of temperature and rainfall, with quadratic temperature and all precipitation models performing well, but the causal influence of each driver remains unclear due to their strong correlation. Further, an independent, mechanistic temperature-dependent R0 model based on laboratory data, which predicts that malaria transmission peaks at 25°C and declines at lower and higher temperatures, aligns well with observed malaria incidence rates, emphasizing the significance and predictability of temperature suitability across scales. By contrast, entomological variables, such as entomological inoculation rate, were not strong predictors of human incidence in this context. Finally, the study explores the predictors of spatial variation in malaria, considering land use, intervention, and entomological variables. The findings contribute to a better understanding of malaria transmission dynamics at local scales, aiding in the development of effective control strategies in endemic regions

The influence of vector‐borne disease on human history: socio‐ecological mechanisms

Vector-borne diseases (VBDs) are embedded within complex socio-ecological systems. While research has traditionally focused on the direct effects of VBDs on human morbidity and mortality, it is increasingly clear that their impacts are much more pervasive. VBDs are dynamically linked to feedbacks between environmental conditions, vector ecology, disease burden, and societal responses that drive transmission. As a result, VBDs have had profound influence on human history. Mechanisms include: (1) killing or debilitating large numbers of people, with demographic and population-level impacts; (2) differentially affecting populations based on prior history of disease exposure, immunity, and resistance; (3) being weaponised to promote or justify hierarchies of power, colonialism, racism, classism and sexism; (4) catalysing changes in ideas, institutions, infrastructure, technologies and social practices in efforts to control disease outbreaks; and (5) changing human relationships with the land and environment. We use historical and archaeological evidence interpreted through an ecological lens to illustrate how VBDs have shaped society and culture, focusing on case studies from four pertinent VBDs: plague, malaria, yellow fever and trypanosomiasis. By comparing across diseases, time periods and geographies, we highlight the enormous scope and variety of mechanisms by which VBDs have influenced human history

Seasonal temperature variation influences climate suitability for dengue, chikungunya, and Zika transmission

<div><p>Dengue, chikungunya, and Zika virus epidemics transmitted by <i>Aedes aegypti</i> mosquitoes have recently (re)emerged and spread throughout the Americas, Southeast Asia, the Pacific Islands, and elsewhere. Understanding how environmental conditions affect epidemic dynamics is critical for predicting and responding to the geographic and seasonal spread of disease. Specifically, we lack a mechanistic understanding of how seasonal variation in temperature affects epidemic magnitude and duration. Here, we develop a dynamic disease transmission model for dengue virus and <i>Aedes aegypti</i> mosquitoes that integrates mechanistic, empirically parameterized, and independently validated mosquito and virus trait thermal responses under seasonally varying temperatures. We examine the influence of seasonal temperature mean, variation, and temperature at the start of the epidemic on disease dynamics. We find that at both constant and seasonally varying temperatures, warmer temperatures at the start of epidemics promote more rapid epidemics due to faster burnout of the susceptible population. By contrast, intermediate temperatures (24–25°C) at epidemic onset produced the largest epidemics in both constant and seasonally varying temperature regimes. When seasonal temperature variation was low, 25–35°C annual average temperatures produced the largest epidemics, but this range shifted to cooler temperatures as seasonal temperature variation increased (analogous to previous results for diurnal temperature variation). Tropical and sub-tropical cities such as Rio de Janeiro, Fortaleza, and Salvador, Brazil; Cali, Cartagena, and Barranquilla, Colombia; Delhi, India; Guangzhou, China; and Manila, Philippines have mean annual temperatures and seasonal temperature ranges that produced the largest epidemics. However, more temperate cities like Shanghai, China had high epidemic suitability because large seasonal variation offset moderate annual average temperatures. By accounting for seasonal variation in temperature, the model provides a baseline for mechanistically understanding environmental suitability for virus transmission by <i>Aedes aegypti</i>. Overlaying the impact of human activities and socioeconomic factors onto this mechanistic temperature-dependent framework is critical for understanding likelihood and magnitude of outbreaks.</p></div

Temperature regimes for major cities during the 2016 calendar year.

<p>Monthly mean temperatures during 2016 were extracted from Weather Underground.</p

Fitted thermal responses for <i>Aedes aegypti</i> life history traits.

<p>Traits were fit to a Brière [] or a quadratic [<i>c</i>(<i>T</i> − <i>T</i>_<i>m</i>)(<i>T</i> − <i>T</i>₀)] function where <i>T</i> represents temperature. <i>T</i>_<i>0</i> and <i>T</i>_<i>m</i> are the critical thermal minimum and maximum, respectively, and <i>c</i> is the rate constant. Thermal responses were fit by [<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0006451#pntd.0006451.ref024" target="_blank">24</a>].</p

Variation in epidemic dynamics by temperature.

<p>The model was simulated under default parameters at four constant temperatures: 20°C, 25°C, 30°C, and 35°C.</p

Epidemiological indices as a function of starting temperature, within a given seasonal temperature regime.

<p>The red curve represents the maximum number of humans in the infected class (<i>I</i>_<i>H</i>) at any given point during the simulation. The blue curve represents the final (or cumulative) epidemic size (<i>R</i>_<i>H</i> at the final time step). The green curve represents the length of the epidemic (i.e., the point at which the number of infected individuals was below one). Here, simulations were run with the temperature conditions: <i>T</i>_<i>min</i> = 10°C, <i>T</i>_<i>mean</i> = 25°C, and <i>T</i>_<i>max</i> = 40°C (A) and <i>T</i>_<i>min</i> = 20°C, <i>T</i>_<i>mean</i> = 25°C, and <i>T</i>_<i>max</i> = 30°C (B).</p

Estimates of epidemic suitability for major cities under different starting temperatures.

<p>Epidemic suitability was calculated as the proportion of the population that became infected in simulations that began at the minimum, mean, or maximum temperature of the seasonal temperature regime. Each city was simulated with its respective temperature regime from the 2016 calendar year with 0% population immunity.</p

Variation in epidemic suitability across different seasonal temperature regimes.

<p>The heat map shows the epidemic suitability (represented as the proportion of the total human population infected during an epidemic) as a function of mean annual temperature and temperature range. Here, temperature range is defined as the seasonal variation about the annual mean temperature. Twenty large, globally important cities are plotted to illustrate their epidemic suitability.</p