Co-circulation and misdiagnosis led to underestimation of the 2015-2017 Zika epidemic in the Americas

Oidtman, Rachel J.; España, Guido; Perkins, T. Alex

doi:10.1101/19010256

Cited by 3 publications

(3 citation statements)

References 47 publications

(57 reference statements)

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“…Lower values of the dispersion parameter indicate overdispersion, such that variability in cases cannot be explained by a single rate of case incidence, as would be generated by a Poisson distribution with rate ρI d,t . Given the likelihood of variation in the reporting probability over the course of the epidemic [5] and across departments [6], we specified a uniform prior for ϕ , which resulted in a level of overdispersion in reporting equal to at least a geometric distribution ( ϕ = 1) but potentially greater ( ϕ < 1).…”

Section: Supplemental Methodsmentioning

confidence: 99%

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Oidtman¹,

Omodei²,

Kraemer³

et al. 2021

Preprint

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When new pathogens emerge, numerous questions arise about their future spread, some of which can be addressed with probabilistic forecasts. The many uncertainties about the epidemiology of emerging pathogens can make it difficult to choose among model structures and assumptions, however. To assess the potential for uncertainties about emerging pathogens to affect forecasts of their spread, we evaluated the performance of a suite of 16 forecasting models in the context of the 2015-2016 Zika epidemic in Colombia. Each model featured a different combination of assumptions about the role of human mobility in driving transmission, spatiotemporal variation in transmission potential, and the number of times the virus was introduced. All models used the same core transmission model and the same iterative data assimilation algorithm to generate forecasts. By assessing forecast performance through time using logarithmic scoring with ensemble weighting, we found that which model assumptions had the most ensemble weight changed through time. In particular, spatially coupled models had higher ensemble weights in the early and late phases of the epidemic, whereas non-spatial models had higher ensemble weights at the peak of the epidemic. We compared forecast performance of the equally-weighted ensemble model to each individual model and identified a trade-off whereby certain individual models outperformed the ensemble model early in the epidemic but the ensemble model outperformed all individual models on average. On balance, our results suggest that suites of models that span uncertainty across alternative assumptions are necessary to obtain robust forecasts in the context of emerging infectious diseases.

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Section: Supplemental Methodsmentioning

confidence: 99%

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Oidtman¹,

Omodei²,

Kraemer³

et al. 2021

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Consequently, these calculations were conditioned upon the assumption of complete observation and perfect diagnoses, so violations therein should be interpreted as the upper bound on the bias that would likely be observed. Finally, I considered a single pathogen in isolation, though misdiagnosis is commonly due to co-circulation of related pathogens (12,13,18). Accounting for the upward bias due to false-positive diagnoses from other pathogens and exploring the magnitude of this effect across epidemiological settings could be important directions for future work.…”

Section: Discussionmentioning

confidence: 99%

“…Crucially, each study modeled variation in surveillance quality through variation in the ascertainment fraction (i.e., the proportion of infections that are detected) and assumed that, once detected, all infections were correctly diagnosed. In reality, however, non-specific clinical and biological features are likely to limit the sensitivity of clinical diagnosis, particularly for emerging infectious diseases (12,13). The extent to which misdiagnosis affects estimates of transmission and burden for pathogens with sub-critical dynamics remains largely unaddressed.…”

Section: Introductionmentioning

confidence: 99%

Misdiagnosis prevents accurate monitoring of transmission and burden for sub-critical pathogens: a case study of Plasmodium knowlesi malaria

Huber

2021

Preprint

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Maintaining surveillance of emerging infectious diseases presents challenges for monitoring their transmission and burden. Incomplete observation of infections and imperfect diagnosis reduce the observed sizes of transmission chains relative to their true sizes. Previous studies have examined the effect of incomplete observation on estimates of pathogen transmission and burden. However, each study assumed that, if observed, each infection was correctly diagnosed. Here, I leveraged principles from branching process theory to examine how misdiagnosis could contribute to bias in estimates of transmission and burden for emerging infectious diseases. Using the zoonotic Plasmodium knowlesi malaria as a case study, I found that, even when assuming complete observation of infections, the number of misdiagnosed cases within a transmission chain for every correctly diagnosed case could range from 0 (0 - 4) when R0 was 0.1 to 86 (0 - 837) when R0 was 0.9. Data on transmission chain sizes obtained using an imperfect diagnostic could consistently lead to underestimates of R0, the basic reproduction number, and simulations revealed that such data on up to 1,000 observed transmission chains was not powered to detect changes in transmission. My results demonstrate that misdiagnosis may hinder effective monitoring of emerging infectious diseases and that sensitivity of diagnostics should be considered in evaluations of surveillance systems.

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Lying in wait: the resurgence of dengue virus after the Zika epidemic in Brazil

et al. 2021

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After the Zika virus (ZIKV) epidemic in the Americas in 2016, both Zika and dengue incidence declined to record lows in many countries in 2017–2018, but in 2019 dengue resurged in Brazil, causing ~2.1 million cases. In this study we use epidemiological, climatological and genomic data to investigate dengue dynamics in recent years in Brazil. First, we estimate dengue virus force of infection (FOI) and model mosquito-borne transmission suitability since the early 2000s. Our estimates reveal that DENV transmission was low in 2017–2018, despite conditions being suitable for viral spread. Our study also shows a marked decline in dengue susceptibility between 2002 and 2019, which could explain the synchronous decline of dengue in the country, partially as a result of protective immunity from prior ZIKV and/or DENV infections. Furthermore, we performed phylogeographic analyses using 69 newly sequenced genomes of dengue virus serotype 1 and 2 from Brazil, and found that the outbreaks in 2018–2019 were caused by local DENV lineages that persisted for 5–10 years, circulating cryptically before and after the Zika epidemic. We hypothesize that DENV lineages may circulate at low transmission levels for many years, until local conditions are suitable for higher transmission, when they cause major outbreaks.

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Co-circulation and misdiagnosis led to underestimation of the 2015-2017 Zika epidemic in the Americas

Cited by 3 publications

References 47 publications

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Trade-offs between individual and ensemble forecasts of an emerging infectious disease

Misdiagnosis prevents accurate monitoring of transmission and burden for sub-critical pathogens: a case study of Plasmodium knowlesi malaria

Lying in wait: the resurgence of dengue virus after the Zika epidemic in Brazil

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