Climatological, virological and sociological drivers of current and projected dengue fever outbreak dynamics in Sri Lanka

Projected impact of climate change on human health in low- and middle-income countries: a systematic review

Low- and middle-income countries (LMICs) contribute relatively little to global carbon emissions but are recognised to be among the most vulnerable parts of the world to health-related consequences of climate change. To help inform resilient health systems and health policy strategies, we sought to systematically analyse published projections of the impact of rising global temperatures and other weather-related events on human health in LMICs. A systematic search involving multiple databases was conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines to identify studies with modelled projections of the future impact of climate change on human health. Qualitative studies, reviews and meta-analyses were excluded. The search yielded more than 2500 articles, of which 70 studies involving 37 countries met criteria for inclusion. China, Brazil and India were the most studied countries while the sub-Saharan African region was represented in only 9% of studies. Forty specific health outcomes were grouped into eight categories. Non-disease-specific temperature-related mortality was the most studied health outcome, followed by neglected tropical infections (predominantly dengue), malaria and cardiovascular diseases. Nearly all health outcomes studied were projected to increase in burden and/or experience a geographic shift in prevalence over the next century due to climate change. Progressively severe climate change scenarios were associated with worse health outcomes. Knowledge gaps identified in this analysis included insufficient studies of various high burden diseases, asymmetric distribution of studies across LMICs and limited use of some climate parameters as independent variables. Findings from this review could be the basis for future research to help inform climate mitigation and adaptation programmes aimed at safeguarding population health in LMICs.

Climate, demography, immunology, and virology combine to drive two decades of dengue virus dynamics in Cambodia

The incidence of dengue virus disease has increased globally across the past half-century, with highest number of cases ever reported in 2019 and again in 2023. We analyzed climatological, epidemiological, and phylogenomic data to investigate drivers of two decades of dengue in Cambodia, an understudied endemic setting. Using epidemiological models fit to a 19-y dataset, we first demonstrate that climate-driven transmission alone is insufficient to explain three epidemics across the time series. We then use wavelet decomposition to highlight enhanced annual and multiannual synchronicity in dengue cycles between provinces in epidemic years, suggesting a role for climate in homogenizing dynamics across space and time. Assuming reported cases correspond to symptomatic secondary infections, we next use an age-structured catalytic model to estimate a declining force of infection for dengue through time, which elevates the mean age of reported cases in Cambodia. Reported cases in >70-y-old individuals in the 2019 epidemic are best explained when also allowing for waning multitypic immunity and repeat symptomatic infections in older patients. We support this work with phylogenetic analysis of 192 dengue virus (DENV) genomes that we sequenced between 2019 and 2022, which document emergence of DENV-2 Cosmopolitan Genotype-II into Cambodia. This lineage demonstrates phylogenetic homogeneity across wide geographic areas, consistent with invasion behavior and in contrast to high phylogenetic diversity exhibited by endemic DENV-1. Finally, we simulate an age-structured, mechanistic model of dengue dynamics to demonstrate how expansion of an antigenically distinct lineage that evades preexisting multitypic immunity effectively reproduces the older-age infections witnessed in our data.

Increasing intensity of enterovirus outbreaks projected with climate change

Pathogens of the enterovirus genus, including poliovirus and coxsackieviruses, typically circulate in the summer months suggesting a possible positive association between warmer weather and transmission. Here we evaluate the environmental and demographic drivers of enterovirus transmission, as well as the implications of climate change for future enterovirus circulation. We leverage pre-vaccination era data on polio in the US as well as data on two enterovirus A serotypes in China and Japan that are known to cause hand, foot, and mouth disease. Using mechanistic modeling and statistical approaches, we find that enterovirus transmission appears positively correlated with temperature although demographic factors, particularly the timing of school semesters, remain important. We use temperature projections from Coupled Model Intercomparison Project Phase 6 (CMIP6) to simulate future outbreaks under late 21st-century climate change for Chinese provinces. We find that outbreak size increases with climate change on average, though results differ across climate models depending on the degree of wintertime warming. In the worst-case scenario, we project peak outbreaks in some locations could increase by up to 40%.

Impact of environmental factors on the spread of dengue fever in Sri Lanka

Dengue fever is a mosquito-borne viral disease caused by the dengue virus of the Flaviviridae family and is responsible for colossal health and economic burden worldwide. This study aimed to investigate the effect of environmental, seasonal, and spatial variations on the spread of dengue fever in Sri Lanka. The study used secondary data of monthly dengue infection and the monthly average of environmental parameters of 26 Sri Lankan regions from January 2015 to December 2019. Besides the descriptive measurements, Kendall's tau_b, Spearman's rho, and Kruskal-Wallis H test have been performed as bivariate analyses. The multivariate generalized linear negative binomial regression model was applied to determine the impacts of meteorological factors on dengue transmission. The aggregate negative binomial regression model disclosed that precipitation (odds ratio: 0.97, p < 0.05), humidity (odds ratio: 1.05, p < 0.01), and air pressure (odds ratio: 1.46, p < 0.01) were significantly influenced the spread of dengue fever in Sri Lanka. The bioclimatic zone is the vital factor that substantially affects the dengue infection, and the wet zone (odds ratio: 6.41, p < 0.05) was more at-risk than the dry zone. The climate season significantly influenced dengue fever transmission, and a higher infection rate was found (odds ratio: 1.46, p < 0.01) in the northeast monsoon season. The findings of this study facilitate policymakers to improve the existing dengue control strategies focusing on the meteorological condition in the local as well as global perspectives.

Infectious disease in an era of global change

The twenty-first century has witnessed a wave of severe infectious disease outbreaks, not least the COVID-19 pandemic, which has had a devastating impact on lives and livelihoods around the globe. The 2003 severe acute respiratory syndrome coronavirus outbreak, the 2009 swine flu pandemic, the 2012 Middle East respiratory syndrome coronavirus outbreak, the 2013-2016 Ebola virus disease epidemic in West Africa and the 2015 Zika virus disease epidemic all resulted in substantial morbidity and mortality while spreading across borders to infect people in multiple countries. At the same time, the past few decades have ushered in an unprecedented era of technological, demographic and climatic change: airline flights have doubled since 2000, since 2007 more people live in urban areas than rural areas, population numbers continue to climb and climate change presents an escalating threat to society. In this Review, we consider the extent to which these recent global changes have increased the risk of infectious disease outbreaks, even as improved sanitation and access to health care have resulted in considerable progress worldwide.

Modelling the interdependent relationships among epidemic antecedents using fuzzy multiple attribute decision making (F-MADM) approaches

With the high incidence of the dengue epidemic in developing countries, it is crucial to understand its dynamics from a holistic perspective. This paper analyzes different types of antecedents from a cybernetics perspective using a structural modelling approach. The novelty of this paper is twofold. First, it analyzes antecedents that may be social, institutional, environmental, or economic in nature. Since this type of study has not been done in the context of the dengue epidemic modelling, this paper offers a fresh perspective on this topic. Second, the paper pioneers the use of fuzzy multiple attribute decision making (F-MADM) approaches for the modelling of epidemic antecedents. As such, the paper has provided an avenue for the cross-fertilization of knowledge between scholars working in soft computing and epidemiological modelling domains.

Cyclic epidemics and extreme outbreaks induced by hydro-climatic variability and memory

A minimalist model of ecohydrologic dynamics is coupled to the well-known susceptible-infected-recovered epidemiological model to explore hydro-climatic controls on infection dynamics and extreme outbreaks. The resulting HYSIR model reveals the existence of a noise-induced bifurcation producing oscillations in infection dynamics. Linearization of the governing equations allows for an analytic expression for the periodicity of infections in terms of both epidemiological (e.g. transmission and recovery rate) and hydrologic (i.e. soil moisture decay rate or memory) parameters. Numerical simulations of the full stochastic, nonlinear system show extreme outbreaks in response to particular combinations of hydro-climatic conditions, neither of which is extreme per se, rather than a single major climatic event. These combinations depend on the assumed functional relationship between the hydrologic variables and the transmission rate. Our results emphasize the importance of hydro-climatic history and system memory in evaluating the risk of severe outbreaks.