Zika virus outbreak in Brazil under current and future climate

Sadeghieh, Tara; Sargeant, Jan M.; Greer, Amy L.; Berke, Olaf; Dueymes, Guillaume; Gachon, Philippe; Ogden, Nicholas H.; Ng, Victoria

doi:10.1016/j.epidem.2021.100491

Cited by 8 publications

(13 citation statements)

References 41 publications

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“…We developed a basic reproduction number ℛ 0 ( T ) as function of temperature-dependent vector parameters, and we used this temperature-dependent ℛ 0 ( T ) to project seasonal disease risk in four Brazilian cities representative of the different climate regions of Brazil. Our work extends and complements existing projections of Zika risk in Brazil [4] and the temperature-dependent reproduction number literature more broadly [17–21] by assessing geographic and year-to-year heterogeneity in projected risk across climate change scenarios.…”

Section: Introductionmentioning

confidence: 67%

“…Zika was introduced in the Americas in 2015 [2], causing numerous outbreaks in countries throughout Latin America, including Brazil, Colombia, and Venezuela. Because vector-borne disease transmission depends on temperature, recent work has outlined the potential for climate change to facilitate its re-emergence (and emergence in new regions) [3][4][5]. Given the concerning health outcomes of Zika -including microcephaly and Guillain-Barre syndrome-the unpredictability of how the changing climate will influence the spread of the virus throughout the western hemisphere is a growing cause of concern.…”

Section: Introductionmentioning

confidence: 99%

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Climate change impacts on Zika and dengue risk in four Brazilian cities: projections using a temperature-dependent basic reproduction number

Wyk

Eisenberg

Brouwer

2022

Preprint

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For vectorborne diseases the basic reproduction number R0, a measure of a disease’s epidemic potential, is highly temperature dependent. Recent work characterizing these temperature dependencies has highlighted how climate change may impact geographic disease spread. We extend this prior work by examining how newly emerging diseases, like Zika will be impacted by specific future climate change scenarios in four diverse regions of Brazil, a country that has been profoundly impacted by Zika. We estimated a temperature-dependent R0(T), derived from a compartmental transmission model, characterizing Zika (and, for comparison, dengue) transmission potential. We obtained historical temperature data for the 5-year period 2015–2019 and projections for 2045–2049 by fitting cubic spline interpolations to data from simulated atmospheric data provided by the CMIP-6 project (specifically, generated by the GFDL-ESM4 model), which provides projections under four Shared Socioeconomic Pathways (SSP). These four SSP scenarios correspond to varying levels of climate change severity. We applied this approach to four Brazilian cities (Manaus, Recife, Rio de Janeiro, and São Paulo) that represent diverse climatic regions. Our model predicts that the R0(T) for Zika peaks at 2.7 around 30?C, while for dengue it peaks at 6.8 around 31?C. We find that the epidemic potential of Zika and dengue will increase beyond current levels in Brazil in all of the climate scenarios. For Manaus, we predict that the annual R0 range will increase from 2.1–2.5, to 2.3–2.7, for Recife we project an increase from 0.4–1.9 to 0.6–2.3, for Rio de Janeiro from 0–1.9 to 0–2.3, and for São Paulo from 0–0.3 to 0–0.7. As Zika immunity wanes and temperatures increase, there will be increasing epidemic potential and longer transmission seasons, especially in regions where transmission is currently marginal. Surveillance systems should be implemented and sustained for early detection.

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Section: Introductionmentioning

confidence: 67%

Section: Introductionmentioning

confidence: 99%

Climate change impacts on Zika and dengue risk in four Brazilian cities: projections using a temperature-dependent basic reproduction number

Wyk

Eisenberg

Brouwer

2022

Preprint

View full text Add to dashboard Cite

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“…aegypti and Ae. albopictus mosquitoes in regions where they were previously absent [116][117][118][119][120][121]. The emergence of CHIKV in the Caribbean islands, a favourite destination for tourists from North America and Europe, creates additional new opportunities for intercontinental transmission of the infection [115].…”

Section: Discussionmentioning

confidence: 99%

The evolutionary and molecular history of a chikungunya virus outbreak lineage

Krambrich,

Mihalič,

Gaunt

et al. 2024

Preprint

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In 2018–2019, Thailand experienced a nationwide spread of chikungunya virus (CHIKV), with approximately 15,000 confirmed cases of disease reported. Here, we investigated the evolutionary and molecular history of the East/Central/South African (ECSA) genotype to determine the origins of the 2018–2019 CHIKV outbreak in Thailand. This was done using newly sequenced clinical samples from travellers returning to Sweden from Thailand in late 2018 and early 2019 and previously published genome sequences. Our phylogeographic analysis showed that before the outbreak in Thailand, the Indian Ocean lineage (IOL) found within the ESCA, had evolved and circulated in East Africa, South Asia, and Southeast Asia for about 15 years. In the first half of 2017, an introduction occurred into Thailand from another South Asian country, most likely Bangladesh, which subsequently developed into a large outbreak in Thailand with export to neighbouring countries. Based on comparative phylogenetic analyses of the complete CHIKV genome and protein modelling, we also identified amino acid substitutions that may be associated with immune evasion, increased spread, and virulence. We identified several mutations in the E1/E2 spike complex, such as E1 K211E and E2 V264A, which are highly relevant as they may lead to changes in vector competence, transmission efficiency and pathogenicity of the virus. A number of mutations (E2 G205S, Nsp3 D372E, Nsp2 V793A), that emerged shortly before the outbreak of the virus in Thailand in 2018 may have altered antibody binding and recognition due to their position. This study not only improves our understanding of the factors contributing to the epidemic in Southeast Asia, but also has implications for the development of effective response strategies and the potential development of new vaccines.

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“…A recent paper modeled the spread of Zika virus using a compartmental model that incorporated spread by mosquitoes (Sadeghieh et al, 2021). We rely on their estimate of a weekly γ, which was 1.2.…”

Section: Simulation Study With Spatially Varying β(S)mentioning

confidence: 99%

“…There is still some clear model misspecification, suggesting that more features may be needed. Alternatively, incorporating spread by mosquitoes from a model like in Sadeghieh et al (2021) along with our spatial spread may yield the best possible results.…”

Section: Simulation Study With Spatially Varying β(S)mentioning

confidence: 99%

A Gaussian-process approximation to a spatial SIR process using moment closures and emulators

Trostle¹,

Guinness²,

Reich³

2022

Preprint

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The dynamics that govern disease spread are hard to model because infections are functions of both the underlying pathogen as well as human or animal behavior. This challenge is increased when modeling how diseases spread between different spatial locations. Many proposed spatial epidemiological models require trade-offs to fit, either by abstracting away theoretical spread dynamics, fitting a deterministic model, or by requiring large computational resources for many simulations. We propose an approach that approximates the complex spatial spread dynamics with a Gaussian process. We first propose a flexible spatial extension to the wellknown SIR stochastic process, and then we derive a moment-closure approximation to this stochastic process. This moment-closure approximation yields ordinary differential equations for the evolution of the means and covariances of the susceptibles and infectious through time. Because these ODEs are a bottleneck to fitting our model by MCMC, we approximate them using a low-rank emulator. This approximation serves as the basis for our hierarchical model for noisy, underreported counts of new infections by spatial location and time. We demonstrate using our model to conduct inference on simulated infections from the underlying, true spatial SIR jump process. We then apply our method to model counts of new Zika infections in Brazil from late 2015 through early 2016.

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Zika virus outbreak in Brazil under current and future climate

Cited by 8 publications

References 41 publications

Climate change impacts on Zika and dengue risk in four Brazilian cities: projections using a temperature-dependent basic reproduction number

Climate change impacts on Zika and dengue risk in four Brazilian cities: projections using a temperature-dependent basic reproduction number

The evolutionary and molecular history of a chikungunya virus outbreak lineage

A Gaussian-process approximation to a spatial SIR process using moment closures and emulators

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