Abstract
When a rare pathogen emerges to cause a pandemic, it is critical to understand its dynamics and the impact of mitigation measures. We use experimental data to parametrize a temperature-dependent model of Zika virus (ZIKV) transmission dynamics and analyse the effects of temperature variability and control-related parameters on the basic reproduction number (R0) and the final epidemic size of ZIKV. Sensitivity analyses show that these two metrics are largely driven by different parameters, with the exception of temperature, which is the dominant driver of epidemic dynamics in the models. Our R0 estimate has a single optimum temperature (≈30°C), comparable to other published results (≈29°C). However, the final epidemic size is maximized across a wider temperature range, from 24 to 36°C. The models indicate that ZIKV is highly sensitive to seasonal temperature variation. For example, although the model predicts that ZIKV transmission cannot occur at a constant temperature below 23°C (≈ average annual temperature of Rio de Janeiro, Brazil), the model predicts substantial epidemics for areas with a mean temperature of 20°C if there is seasonal variation of 10°C (≈ average annual temperature of Tampa, Florida). This suggests that the geographical range of ZIKV is wider than indicated from static R0 models, underscoring the importance of climate dynamics and variation in the context of broader climate change on emerging infectious diseases.
Highlights
Vector-borne viruses are emerging threats to both human and animal health
To better understand potential climate effects on control strategies for Zika virus (ZIKV), we developed a temperature-dependent dynamical model based on recent experimental work characterizing temperature–trait relationships between ZIKV vector competence, extrinsic incubation rate, and the per capita daily mosquito mortality rate [41]
Our results show that R0 and the final epidemic size were largely driven by different sets of parameters, with the exception of temperature being the dominant driver of both
Summary
Vector-borne viruses (arboviruses) are emerging threats to both human and animal health. The global expansions of dengue virus (DENV), West Nile virus (WNV), chikungunya (CHIKV) and most recently Zika virus (ZIKV) are prominent examples of how quickly mosquito-transmitted viruses can emerge and spread through naive host populations. Developing tools that allow us to successfully predict outbreaks of these viruses and efficiently target current and future interventions to specific times and locations can aid effective mosquito and disease control. Such efforts are often limited by gaps in knowledge on the relationships among mosquito vectors, pathogens and the environment, especially for emerging arboviruses such as CHIKV and ZIKV. Even in well-researched disease systems (e.g. malaria and DENV), key transmission parameters are only estimated from a few studies [3,4,5]
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