Translating Predictions of Zoonotic Viruses for Policymakers

LeBreton, Matthew; Fuller, Trevon; Hoffman, Risa M.; Njabo, Kevin Y.; Brewer, Timothy F.; Dibongue, Elsa; Diffo, Joseph Le Doux; Kameni, Jean-Marc Feussom; Loul, Sévérin; Nchinda, Godwin; Njouom, Richard; Nwobegahay, Julius; Takuo, Jean Michel; Torimiro, Judith; Wade, Abel; Smith, Thomas B.

doi:10.1007/s10393-017-1304-3

Cited by 13 publications

(16 citation statements)

References 39 publications

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“…Finally, while we believe our model captures RVF’s climatic relationships well, further research could be done into regional, seasonal suitability patterns. Public health and veterinary health workers can triangulate our maps with other types of risk assessments and engage with local experts to make the most informed decisions for RVF prevention ( Judson et al, 2018 ). More studies that use and produce season-specific data and results would help decision-makers to best prepare for RVF.…”

Section: Discussionmentioning

confidence: 99%

Informing Rift Valley Fever preparedness by mapping seasonally varying environmental suitability

Hardcastle

Osborne

Ramshaw

et al. 2020

International Journal of Infectious Diseases

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Highlights Database of Rift Valley Fever occurrences from 46 countries over 22 years. Predictions of Rift Valley Fever suitability for every month over 1995–2016. Identifies areas at-risk by synthesizing time-series of environmental predictions. We use human and livestock data to identify possible hotspots of disease spillover. We identify places where long-term and routine preparation efforts should be focused.

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Section: Discussionmentioning

confidence: 99%

Informing Rift Valley Fever preparedness by mapping seasonally varying environmental suitability

Hardcastle

Osborne

Ramshaw

et al. 2020

International Journal of Infectious Diseases

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“…S7). More broadly, niche maps for filoviral and other zoonotic diseases with wildlife origins rely on incompletely observed human/primate outbreak data for validation 35 . The Ebola virus spillover map used to generate Figure 4-B, for example, was fit to known instances of spillover in people (either directly from the putative reservoir or via intermediate hosts such as apes and duikers) 24 .…”

Section: Discussionmentioning

confidence: 99%

“…The yellow fever virus map for Africa is comparatively newer, and model uncertainty is still high in East and Central Africa, where occurrence data is comparatively sparse 19 . Minor differences in niche model parameterization can produce major downstream differences in model-based inference 35,40 ; future work can capture this uncertainty by combining risk maps from several sources.…”

Section: Discussionmentioning

confidence: 99%

“…Similarly, future work may benefit from using models that are spatially tailored to the area of interest 35 , or incorporate other dimensions of spatiotemporal heterogeneity like seasonal spillover risk. These dimensions might be especially informative for ruling out arboviral diseases 41,42 , but could also be informative for filoviruses as spillover drivers become clearer 43 .…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

Syndromic detectability of haemorrhagic fever outbreaks

Glennon

Jephcott

Oti

et al. 2020

Preprint

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Late detection of emerging viral transmission allows outbreaks to spread uncontrolled, the devastating consequences of which are exemplified by recent epidemics of Ebola virus disease. Especially challenging in places with sparse healthcare, limited diagnostic capacity, and public health infrastructure, syndromes with overlapping febrile presentations easily evade early detection. There is a clear need for evidence-based and context-dependent tools to make syndromic surveillance more efficient. Using published data on symptom presentation and incidence of 21 febrile syndromes, we develop a novel algorithm for aetiological identification of case clusters and demonstrate its ability to identify outbreaks of dengue, malaria, typhoid fever, and meningococcal disease based on clinical data from past outbreaks. We then apply the same algorithm to simulated outbreaks to systematically estimate the syndromic detectability of outbreaks of all 21 syndromes. We show that while most rare haemorrhagic fevers are clinically distinct from most endemic fevers in sub-Saharan Africa, VHF detectability is limited even under conditions of perfect syndromic surveillance. Furthermore, even large clusters (20+ cases) of filoviral diseases cannot be routinely distinguished by the clinical criteria present in their case definitions alone; we show that simple syndromic case definitions are insensitive to rare fevers across most of the region. We map the estimated detectability of Ebola virus disease across sub-Saharan Africa, based on geospatially mapped estimates of malaria, dengue, and other fevers with overlapping syndromes. We demonstrate "hidden hotspots" where Ebola virus is likely to spill over from wildlife and also transmit undetected for many cases. Such places may represent both the locations of past unobserved outbreaks and potential future origins for larger epidemics. Finally, we consider the implications of these results for improved locally relevant syndromic surveillance and the consequences of syndemics and under-resourced health infrastructure for infectious disease emergence.

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“…Ensuring that the science is useful outside academia, through acts of translation and engagement with the worlds of policy and day-to-day government vector control, is also fraught with troubles. Working in Cameroon on emerging zoonoses, Judson et al (2018) explored the utility of disease models, constructed mostly by outside academic groups, for public health experts and found that most of the experts were unfamiliar with the academic models. The models typically lacked essential contextualized (local) knowledge that would have made them more detailed and accurate; for instance, of environmental boundaries and basic administrative regions.…”

Section: Introductionmentioning

confidence: 99%

Locating Zika

Bardosh¹

2019

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The emergence of Zika virus in 2015 challenged conventional ideas of mosquitoborne diseases, tested the resilience of health systems and embedded itself within local sociocultural worlds, with major implications for environmental, sexual, reproductive and paediatric health. This book explores this complex viral epidemic and situates it within its broader social, epidemiological and historical context in Latin America and the Caribbean. The chapters include a diverse set of case studies from scholars and health practitioners working across the region, from Brazil, Venezuela, Ecuador, Mexico, Colombia, the United States and Haiti. The book explores how mosquito-borne disease epidemics (not only Zika but also Chikungunya, Dengue and malaria) intersect with social change and health governance. By doing so, the authors reflect on the ways in which situated knowledge and social science approaches can contribute to more effective health policy and practice for mosquito-borne disease threats in a changing world.

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Translating Predictions of Zoonotic Viruses for Policymakers

Cited by 13 publications

References 39 publications

Informing Rift Valley Fever preparedness by mapping seasonally varying environmental suitability

Informing Rift Valley Fever preparedness by mapping seasonally varying environmental suitability

Syndromic detectability of haemorrhagic fever outbreaks

Locating Zika

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