Understanding the Epidemic Course in Order to Improve Epidemic Forecasting

• Published in: Geohealth 2020-10, Vol.4 (10), p.e2020GH000303-n/a
• Main Author: Jia, Peng
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Big Data; Chronic illnesses; Conflicts of interest; COVID-19; Crowdsourcing; Disease; Earthquake Interaction, Forecasting, and Prediction; Epidemics; Epidemiology; Estimation and Forecasting; Forecasting; General or Miscellaneous; Geohealth; Hydrology; Infections; Informatics; Information sharing; Magnetospheric Physics; Mathematical Geophysics; Mathematical models; Monitoring, Forecasting, Prediction; Natural Hazards; Ocean Predictability and Prediction; Oceanography: General; Pandemics; Policy s; Prediction; Probabilistic Forecasting; Public Health; Seismology; Severe acute respiratory syndrome; Space Weather; Spatial Analysis; Spatial data; Spatial Modeling; Spectral Analysis; Statistical Analysis; Statistical methods: Descriptive; Statistical methods: Inferential; The COVID‐19 Pandemic: Linking Health, Society and Environment
• ISSN: 2471-1403; 2471-1403
• DOI: 10.1029/2020GH000303

The epidemic course of the severe acute respiratory syndrome (SARS) has been differently divided according to its transmission pattern and the infection and mortality status. Unfortunately, such efforts for the coronavirus disease 2019 (COVID‐19) have been lacking. Does every epidemic have a unique epidemic course? Can we coordinate two arbitrary courses into an integrated course, which could better reflect a common real‐world progression pattern of the epidemics? To what degree can such arbitrary divisions help predict future trends of the COVID‐19 pandemic and future epidemics? Spatial lifecourse epidemiology provides a new perspective to understand the course of epidemics, especially pandemics, and a new toolkit to predict the course of future epidemics on the basis of big data. In the present data‐driven era, data should be integrated to inform us how the epidemic is transmitting at the present moment, how it will transmit at the next moment, and which interventions would be most cost‐effective to curb the epidemic. Both national and international legislations are needed to facilitate the integration of relevant policies of data sharing and confidentiality protection into the current pandemic preparedness guidelines. Plain Language Summary The period of the severe acute respiratory syndrome (SARS) epidemic has been divided according to its transmission pattern and the infection and mortality status. Unfortunately, such efforts for the coronavirus disease 2019 (COVID‐19) have been lacking. Does every epidemic have a unique pattern? Can we find out a common real‐world progression pattern of the epidemics? To what degree can such arbitrary divisions help predict future trends of the COVID‐19 pandemic and future epidemics? The advanced spatial and digital technologies provide a new perspective to understand the transmission patterns of epidemics, especially pandemics, and a new toolkit to predict the progression of future epidemics on the basis of big data. In the present data‐driven era, data should be integrated to inform us how the epidemic is transmitting at the present moment, how it will transmit at the next moment, and which interventions would be most cost‐effective to curb the epidemic. Both national and international legislations are needed to facilitate the integration of relevant policies of data sharing and confidentiality protection into the current pandemic preparedness guidelines. Key Points We need to coordinate unique epidemic courses int