Early detection of COVID-19 outbreaks using human mobility data
| Autor: | Yotam Dery, Margaret L. Brandeau, Irad Ben-Gal, Dan Yamin, Matan Yechezkel, Grace Guan |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
Viral Diseases
Computer science Social Sciences 01 natural sciences 010305 fluids & plasmas Geographical Locations 0302 clinical medicine Medical Conditions Mathematical and Statistical Techniques Goodness of fit Statistics Medicine and Health Sciences Public and Occupational Health 030212 general & internal medicine Israel Virus Testing Multidisciplinary Geography Socioeconomic Aspects of Health Test (assessment) Infectious Diseases Categorization Physical Sciences Engineering and Technology Regression Analysis Medicine Research Article Asia Coronavirus disease 2019 (COVID-19) Mean squared error Lag Science Equipment Linear Regression Analysis Research and Analysis Methods Human Geography 03 medical and health sciences Diagnostic Medicine 0103 physical sciences Linear regression Humans Statistical Methods Set (psychology) Communication Equipment Outbreak COVID-19 Covid 19 Health Care People and Places Communicable Disease Control Earth Sciences Human Mobility Cell Phones Mathematics Forecasting |
| Zdroj: | PLoS ONE, Vol 16, Iss 7, p e0253865 (2021) PLoS ONE |
| ISSN: | 1932-6203 |
| Popis: | Background Contact mixing plays a key role in the spread of COVID-19. Thus, mobility restrictions of varying degrees up to and including nationwide lockdowns have been implemented in over 200 countries. To appropriately target the timing, location, and severity of measures intended to encourage social distancing at a country level, it is essential to predict when and where outbreaks will occur, and how widespread they will be. Methods We analyze aggregated, anonymized health data and cell phone mobility data from Israel. We develop predictive models for daily new cases and the test positivity rate over the next 7 days for different geographic regions in Israel. We evaluate model goodness of fit using root mean squared error (RMSE). We use these predictions in a five-tier categorization scheme to predict the severity of COVID-19 in each region over the next week. We measure magnitude accuracy (MA), the extent to which the correct severity tier is predicted. Results Models using mobility data outperformed models that did not use mobility data, reducing RMSE by 17.3% when predicting new cases and by 10.2% when predicting the test positivity rate. The best set of predictors for new cases consisted of 1-day lag of past 7-day average new cases, along with a measure of internal movement within a region. The best set of predictors for the test positivity rate consisted of 3-days lag of past 7-day average test positivity rate, along with the same measure of internal movement. Using these predictors, RMSE was 4.812 cases per 100,000 people when predicting new cases and 0.79% when predicting the test positivity rate. MA in predicting new cases was 0.775, and accuracy of prediction to within one tier was 1.0. MA in predicting the test positivity rate was 0.820, and accuracy to within one tier was 0.998. Conclusions Using anonymized, macro-level data human mobility data along with health data aids predictions of when and where COVID-19 outbreaks are likely to occur. Our method provides a useful tool for government decision makers, particularly in the post-vaccination era, when focused interventions are needed to contain COVID-19 outbreaks while mitigating the collateral damage from more global restrictions. |
| Databáze: | OpenAIRE |
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