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

Abstract

25 26 27 INTRODUCTION: Epidemic forecasting and prediction tools have the potential to provide 28 actionable information in the midst of emerging epidemics. While numerous predictive studies 29 were published during the 2016-2017 Zika Virus (ZIKV) pandemic, it remains unknown how 30 timely, reproducible and actionable the information produced by these studies was. METHODS: 31 To improve the functional use of mathematical modeling in support of future infectious disease 32 outbreaks, we conducted a systematic review of all ZIKV prediction studies published during the 33 recent ZIKV pandemic using the PRISMA guidelines. Using MEDLINE, EMBASE and grey 34 literature review, we identified studies that forecasted, predicted or simulated ecological or 35 epidemiological phenomenon related to the Zika pandemic that were published as of March 01, 36 2017. Eligible studies underwent evaluation of objectives, data sources, methods, timeliness, 37 reproducibility, accessibility and clarity by independent reviewers. RESULTS: 2034 studies were 38 identified, of which n = 73 met eligibility criteria. Spatial spread, R 0 (basic reproductive number) 39 and epidemic dynamics were most commonly predicted, with few studies predicting Guillain-40 Barré Syndrome burden (4%), sexual transmission risk (4%) and intervention impact (4%). Most 41 studies specifically examined populations in the Americas (52%), with few African-specific 42 studies (4%). Case count (67%), vector (41%) and demographic data (37%) were the most 43 common data sources. Real-time internet data and pathogen genomic information were used in 44 7% and 0% of studies, respectively, and social science and behavioral data were typically absent 45 in modeling efforts. Deterministic models were favored over stochastic approaches. Forty 46 percent of studies made model data entirely available, 29% provided all relevant model code, 47 43% presented uncertainty in all predictions and 54% provided sufficient methodological detail 48 allowing complete reproducibility. Fifty-one percent of predictions were published after the 49 epidemic peak in the Americas. While the use of preprints improved the accessibility of ZIKV 50 predictions by a median 119 days sooner than journal publication dates, they were used in only 51 30% of studies. CONCLUSIONS: Many ZIKV predictions were published during the 2016-2017 52 pandemic. The accessibility, reproducibility, timeliness, and incorporation of uncertainty in 53 these published predictions varied and indicates that there is substantial room for 54 improvement. To enhance the utility of analytical tools for outbreak response, it is essential to 55 improve the sharing of model data, code, and preprints for future outbreaks, epidemics and 56 pandemics.57 58 Author summary: Researchers published many studies which sought to predict and forecast 59 important features of Zika virus (ZIKV) infections and their spread during the 2016-2017 ZIKV60 pandemic. We conducted a comprehensive review of such ZIKV prediction studies and 61 evaluated their ...

A seq2seq model to forecast the COVID-19 cases, deaths and reproductive R numbers in US counties

“…Uncertainty in forecasting models is common as had been observed in in uenza epidemic and other serious outbreaks such as the Ebola, and Zika viruses [9][10][11][12]. Many complex and unknown features contribute to virus spread, disease severity and mortality.…”

Combining probabilistic forecasts of COVID-19 mortality in the United States

“…Forecasting methods are well established in providing predictions of uncertain events to decision makers across a variety of settings, ranging from energy providers and individuals relying on the weather outlook, to investors eager to gain insight into future economic conditions. Epidemiological forecasting models have been applied to both vector-borne diseases, including Dengue disease ( Shi et al, 2016 ) and the Zika virus ( Kobres et al, 2019 ), and contagious infectious diseases. These include the Severe Acute Respiratory Syndrome (SARS) ( Ng et al, 2003 ), Ebola ( Viboud et al, 2017 ) and the Middle East respiratory syndrome (MERS) (Da'ar et al, 2018).…”

Fast Evaluation of Viral Emerging Risks (FEVER): A computational tool for biosurveillance, diagnostics, and mutation typing of emerging viral pathogens

“…However, RNA viruses mutate rapidly, so biosurveillance tools must be able to detect groups of viral pathogens with diverse genome sequences and emerging variants, often requiring timeconsuming and expensive procedures performed by trained personnel that are not easily accessible in all regions of the world (11,12). Once spillover occurs and a viral isolate enters human circulation, a rapid and accurate diagnostic test is required to circumvent its spread (13)(14)(15)(16).…”