Consensus and conflict among ecological forecasts of Zika virus outbreaks in the United States

Abstract

Ecologists are increasingly involved in the pandemic prediction process. In the course of the Zika outbreak in the Americas, several ecological models were developed to forecast the potential global distribution of the disease. Conflicting results produced by alternative methods are unresolved, hindering the development of appropriate public health forecasts. We compare ecological niche models and experimentally-driven mechanistic forecasts for Zika transmission in the continental United States, a region of high model conflict. We use generic and uninformed stochastic county-level simulations to demonstrate the downstream epidemiological consequences of conflict among ecological models, and show how assumptions and parameterization in the ecological and epidemiological models propagate uncertainty and produce downstream model conflict. We conclude by proposing a basic consensus method that could resolve conflicting models of potential outbreak geography and seasonality. Our results illustrate the unacceptable and often undocumented margin of uncertainty that could emerge from using any one of these predictions without reservation or qualification. In the short term, ecologists face the task of developing better post hoc consensus that accurately forecasts spatial patterns of Zika virus outbreaks. Ultimately, methods are needed that bridge the gap between ecological and epidemiological approaches to predicting transmission and realistically capture both outbreak size and geography.

A systematic review and evaluation of Zika virus forecasting and prediction research during a public health emergency of international concern

“…In contrast to existing reviews on models developed during the ZIKV pandemic, which described specific contributions of modeling (112) or validated analytical assessment of results (113), this systematic review focused on capturing lessons that could improve the functional use of mathematical modeling in support of future infectious disease outbreaks. Extending an approach used by Chretien et al in their evaluation of Ebola models, we focused on aspects of the studies that likely are particularly relevant to their usefulness during an outbreak (103).…”

Global expansion and redistribution of Aedes-borne virus transmission risk with climate change

“…Climate change is likely to change the relationship between transmission risk and disease burden at fine scales within those zones of transmission nonlinearly, such that areas with shorter seasons of transmission could still experience increased overall disease burdens, or vice versa. Combining broad spatial models with finer-scale models of attack rates or outbreak size is a critical step towards bridging scales [58,79], but more broadly, building consensus and revealing similarities and differences between all available models via transparency, is of paramount importance [80].…”

Numerical Study on Zika Epidemic Early Warning Algorithms Driven by Dynamical Network Biomarker

“…As we know, some existing work about this topic is also interesting, such as References [19,20]. These studies used ecological niche models (ENMs) to map the possible distribution of Zika virus, utilizing a different combination of occurrence data, environmental predictors, and statistical approaches.…”

“…These studies used ecological niche models (ENMs) to map the possible distribution of Zika virus, utilizing a different combination of occurrence data, environmental predictors, and statistical approaches. And it is shown in [20] that the results obtained by generic and uniformed stochastic county-level simulations illustrate a basic consensus method, which can resolve conflicting models of potential outbreak geography and seasonality in the United States. However, our results illustrate that the forecast of the outbreak of Zika by dynamical network biomarker relies on the protein of Zika virus, and the early warning index is also established by the property of DNB molecule.…”

“…This is to say, our method mainly depends on dynamical network biomarker, such that it can be less influenced by uncertain factors. The method in [20] focuses on the United States as a test system and depends on many random factors, including environmental variable and population-at-risk. Therefore, we have the same purpose, i.e., to forecast the outbreak of Zika epidemic, but by different methods.…”