Potential Distribution of Dengue Fever Under Scenarios of Climate Change and Economic Development

Abstract

Dengue fever is the most important viral vector-borne disease with ~50 million cases per year globally. Previous estimates of the potential effect of global climate change on the distribution of vector-borne disease have not incorporated the effect of socioeconomic factors, which may have biased the results. We describe an empirical model of the current geographic distribution of dengue, based on the independent effects of climate and gross domestic product per capita (GDPpc, a proxy for socioeconomic development). We use the model, along with scenario-based projections of future climate, economic development, and population, to estimate populations at risk of dengue in the year 2050. We find that both climate and GDPpc influence the distribution of dengue. If the global climate changes as projected but GDPpc remained constant, the population at risk of dengue is estimated to increase by about 0.28 billion in 2050. However, if both climate and GDPpc change as projected, we estimate a decrease of 0.12 billion in the population at risk of dengue in 2050. Empirically, the geographic distribution of dengue is strongly dependent on both climatic and socioeconomic variables. Under a scenario of constant GDPpc, global climate change results in a modest but important increase in the global population at risk of dengue. Under scenarios of high GDPpc, this adverse effect of climate change is counteracted by the beneficial effect of socioeconomic development.

Space-time scan statistics of 2007-2013 dengue incidence in Cimahi city, Indonesia

“…For example, many different associations of DF outbreaks with various climate and socio-economic variables have been reported (Johansson et al, 2009;Banu et al, 201l;Åström et al, 2012;Oki and Yamamoto, 2012;Ariati and Musadad, 2013;Naish et al, 2014;Ariati and Anwar, 2014;Bouzid et al, 2014;Dhimal et al, 2015). A study on knowledge, attitude, and practices in Cimahi was performed by Pradani et al (2010), who concluded that people often show inconsistent preventive measure practices in spite of good perception and knowledge of DF risk factors.…”

Climate change and vector-borne diseases: what are the implications for public health research and policy?

“…As for malaria, there is very strong laboratory and field evidence for sensitivity to meteorological variables, and for the modifying effect of non-climatic factors. Modelling studies also suggest that climate change has favoured, and will continue to favour dengue transmission [22,23]. By contrast, the evidence of the protective effect of either general socioeconomic development, or specific disease control measures, is much weaker than for malaria.…”

“…In recent years, there have been improvements in both the degree to which such models are validated against observed distributions and incidence in the past [20,41], and the extent to which they incorporate both the independent and interactive effects of non-climatic factors, such as changes in population size and distribution (e.g. urbanization rates), and economic development [23]. Such studies can therefore give broad indications of potential future effects of climate change, of the relative importance of climate versus other determinants, and of different diseases, and indicate areas that are likely to become more or less suitable for transmission in the future.…”

Potential future risk of cholera due to climate change in northern Nigeria

“…This statistical model was developed and validated in (Leckebusch & Abdussalam, 2015) using Generalized Additive Models (GAMs) approach which can better account for the seasonally-varying influence of additional climatic and non-climatic influences that may influence the disease (the model has been improved with updated disease data for the purpose of this study). GAM has been used for projection studies (e.g., Astrom et al, 2012). These models were developed based on monthly aggregate of clinically-diagnosed cases of cholera from three selected hospitals (Kano, Sokoto and Gusau), and monthly weather variables from nearby meteorological stations.…”