Climate drives spatial variation in Zika epidemics in Latin America

Harris, Mallory; Caldwell, Jamie M.; Mordecai, Erin A.

doi:10.1098/rspb.2019.1578

Cited by 23 publications

(18 citation statements)

References 60 publications

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“…The spatial variation of ZIKV in Colombia indeed has been verified in the literature [37–39], while no direct evidence shows that the spatial variation of ZIKV was associated with climate change. Our findings on the best meteorological predictors are consistent with the results of a previous study, which analyzed the data in the whole Latin America [40]. In this study, the meteorological predictors can only explain 36-39% of the spatial variation of ZIKV, revealing that there are still additional non-meteorological factors which can explain the spatial variation of ZIKV.…”

Section: Discussionsupporting

confidence: 91%

Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

Chien

Pérez

2019

BMC Infect Dis

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BackgroundSeveral Zika virus (ZIKV) outbreaks have occurred since October 2015. Because there is no effective treatment for ZIKV infection, developing an effective surveillance and warning system is currently a high priority to prevent ZIKV infection. Despite Aedes mosquitos having been known to spread ZIKV, the calculation approach is diverse, and only applied to local areas. This study used meteorological measurements to monitor ZIKV infection due to the high correlation between climate change and Aedes mosquitos and the convenience to obtain meteorological data from weather monitoring stations.MethodsThis study applied the Bayesian structured additive regression modeling approach to include spatial interactive terms with meteorological factors and a geospatial function in a zero-inflated Poisson model. The study area contained 32 administrative departments in Colombia from October 2015 to December 2017. Weekly ZIKV infection cases and daily meteorological measurements were collected. Mapping techniques were adopted to visualize spatial findings. A series of model selections determined the best combinations of meteorological factors in the same model.ResultsWhen multiple meteorological factors are considered in the same model, both total rainfall and average temperature can best assess the geographic disparities of ZIKV infection. Meanwhile, a 1-in. increase in rainfall is associated with an increase in the logarithm of relative risk (logRR) of ZIKV infection of at most 1.66 (95% credible interval [CI] = 1.09, 2.15) as well as a 1 °F increase in average temperature is significantly associated with at most 0.79 (95% CI = 0.12, 1.22) increase in the logRR of ZIKV. Moreover, after controlling rainfall and average temperature, an independent geospatial function in the model results in two departments with an excessive ZIKV risk which may be explained by unobserved factors other than total rainfall and average temperature.ConclusionOur study found that meteorological factors are significantly associated with ZIKV infection across departments. The study determined both total rainfall and average temperature as the best meteorological factors to identify high risk departments of ZIKV infection. These findings can help governmental agencies monitor at risk areas according to meteorological measurements, and develop preventions in those at risk areas in priority.

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Section: Discussionsupporting

confidence: 91%

Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

Chien

Pérez

2019

BMC Infect Dis

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“…Prediction of climatic conditions in advance can help in forecasting the emergence and propagation of pathogenic viruses and bacteria or their vectors. Harris et al ( 2019 ) showed that climate conditions i.e. relative humidity, temperature, and the population are the key determinants related to the occurrence of an endemic in a particular geographical region.…”

Section: Environmental Issues and The Emergence Of Pandemicsmentioning

confidence: 99%

Linkages between environmental issues and zoonotic diseases: with reference to COVID-19 pandemic

Mishra

Arora

2021

Environmental Sustainability

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Coronavirus disease (COVID-19) caused by severe acute respiratory syndrome (SARS) coronavirus 2 (SARS-CoV-2) has taken mankind by surprise with various unprecedented impacts on human life around the globe. This zoonotic pandemic is proving to be the most destructive disease outbreaks since decades. The increasing human population and anthropogenic activities have impacted the environment and have direct linkages with the current and other recent outbreaks of zoonotic diseases. Despite having a difference in their origin, major reasons behind the emergence and spread of zoonotic pandemics are related to activities such as habitat fragmentation, deforestation, biodiversity loss, intensive agriculture and livestock farming, uncontrolled urbanization, pollution, climate change and bushmeat hunting and trading. It is important to focus on environmental and climatic factors that are involved in the emergence of such pandemics involving novel human pathogens and viruses in particular. Research and data analysis, particularly in relation to COVID-19, has shown that meteorological factors along with population density and living conditions (particularly in the urban and semi-urban areas) play a crucial role in the intensity, evolution and spread of SARS-CoV-2. This particular virus is novel but coronaviruses have a long history and are known to cause disease outbreaks earlier as well. COVID-19 pandemic provides learning for the future, in particular the importance of environmental sustainability for controlling such outbreaks. A strategic plan can be developed involving policy-makers, organizations, and governments to control the onset and spread of the novel pathogens. This review-based study recommends that prevention of COVID-19 like pandemics from re-occurring is through tackling the issues related to the environment by controlling anthropogenic activities. It will also be important to track the lineage and future evolution course of such human pathogens so as to determine the nexus of environmental and biological factors in the development and spread of novel strains.

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“…In the first equation, the cumulative distribution function of the ZIKV generation time was denoted by F ZIKV . This was estimated by Ferguson et al and subsequently used by Harris et al to obtain the weight parameters ω u 's [10,46]. In the second equation, M units.…”

Section: Susceptible Spatial Units With Cryptic Spread Of Zikvmentioning

confidence: 99%

Mapping the cryptic spread of the 2015–2016 global Zika virus epidemic

Sun

Dickens

Jit

et al. 2020

BMC Med

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Background Zika virus (ZIKV) emerged as a global epidemic in 2015–2016 from Latin America with its true geographical extent remaining unclear due to widely presumed underreporting. The identification of locations with potential and unknown spread of ZIKV is a key yet understudied component for outbreak preparedness. Here, we aim to identify locations at a high risk of cryptic ZIKV spread during 2015–2016 to further the understanding of the global ZIKV epidemiology, which is critical for the mitigation of the risk of future epidemics. Methods We developed an importation simulation model to estimate the weekly number of ZIKV infections imported in each susceptible spatial unit (i.e. location that did not report any autochthonous Zika cases during 2015–2016), integrating epidemiological, demographic, and travel data as model inputs. Thereafter, a global risk model was applied to estimate the weekly ZIKV transmissibility during 2015–2016 for each location. Finally, we assessed the risk of onward ZIKV spread following importation in each susceptible spatial unit to identify locations with a high potential for cryptic ZIKV spread during 2015–2016. Results We have found 24 susceptible spatial units that were likely to have experienced cryptic ZIKV spread during 2015–2016, of which 10 continue to have a high risk estimate within a highly conservative scenario, namely, Luanda in Angola, Banten in Indonesia, Maharashtra in India, Lagos in Nigeria, Taiwan and Guangdong in China, Dakar in Senegal, Maputo in Mozambique, Kinshasa in Congo DRC, and Pool in Congo. Notably, among the 24 susceptible spatial units identified, some have reported their first ZIKV outbreaks since 2017, thus adding to the credibility of our results (derived using 2015–2016 data only). Conclusion Our study has provided valuable insights into the potentially high-risk locations for cryptic ZIKV circulation during the 2015–2016 pandemic and has also laid a foundation for future studies that attempt to further narrow this key knowledge gap. Our modelling framework can be adapted to identify areas with likely unknown spread of other emerging vector-borne diseases, which has important implications for public health readiness especially in resource-limited settings.

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Climate drives spatial variation in Zika epidemics in Latin America

Cited by 23 publications

References 60 publications

Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

Identifying high risk areas of Zika virus infection by meteorological factors in Colombia

Linkages between environmental issues and zoonotic diseases: with reference to COVID-19 pandemic

Mapping the cryptic spread of the 2015–2016 global Zika virus epidemic

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