The recent coronavirus in China has recalled previous outbreaks such as SARS from nearly two decades ago that affected many countries and killed hundreds of people. Since the early 2000s, computational methods and mapping have significantly improved, providing more accurate spatial forecasts and even predictions as to how epidemics may spread. Such forecasts may prove useful if the recent coronavirus or other viruses spread.
Avian flu occurred about seven years ago in China and, using data from that work, researchers were able to map the spread of the virus and forecast where it would likely migrate to. In this case, mapping the virus and how it spread helped to show what factors likely instigated its spread. The avian flu, similar to the recent coronavirus, appeared in a poultry market with high human population density and near irrigated croplands. The area, similarly, had a lot of built-up land, with this region experiencing relatively high humidity and temperatures at the time. In this case, mapping different layers of data on built and natural layers enabled a boosted regression tree approach to demonstrate the likely factors that created conditions for the virus to spread.
While this earlier study enabled an assessment of the factors that led to the spread of avian flu, more recent efforts are trying to forecast in real-time the spread of epidemics. A relatively recent assessment highlighted the challenges, with key factors that need to be measured quickly including mobility of the population and virus, susceptibility of the population and environment, how transmittable pathogens are, population density in a given area, and an affected area’s healthcare capacity. These factors, in some cases such as population density, can be measured prior to any outbreak, but other factors such as transmitability of a pathogen are difficult to determine until an outbreak occurs, highlighting challenges of predicting outbreaks. Although there are challenges for modelling real-timer epidemic spread, various susceptible areas for given types of viruses, such as coronaviruses, could be determined a priori to an outbreak that could be used to alert healthcare officials.
Tools for Mapping and Predicting Epidemics
ProMED and HealthMap are statistical tools used to forecast potential virus outbreaks. In fact, recent work has incorporated statistical modeling, testing it with the Africa Ebola epidemic from 2013-2016, to demonstrate that it is possible to predict in advance the likely outbreak of epidemics between 1-4 weeks prior to an outbreak. This is possible by estimating the underlying factors for a given virus or disease, estimating human factors such as population density, and then estimating for a given place if a specific type of virus could likely outbreak there at a given time, similar to other studies but this time controlling for a specific type of virus. While the tool does not enable one to know for sure when an outbreak could occur, while also not addressing all types of viruses, it can potentially give some advanced warning to healthcare professionals to better prepare an area for a possible outbreak for at least some viruses we have a better understanding of. Recent work is attempting to integrate genomic sequencing, which helps to determine rate and likelihood of mutation in viruses that make previous strains not affect humans able to infect people, along with modeling and forecasting tools that use a combination of machine learning and statistical modeling, integrating human, built environment, and environmental factors in spread.
Forecasting the spread of viruses and mapping potential epidemics is gaining important attention in the medical community. There is greater awareness that our modern lifestyle of travel and high population density in many regions makes human populations particularly vulnerable to epidemics. New techniques for integrating healthcare tools with forecasting capabilities using a variety of modeling and machine learning methods could potentially help mitigate impacts of future viruses. The recent outbreak of the coronavirus indicates we are not at the point where we can prevent outbreaks, but we can better understand where they are likely to occur.
 For more on mapping the avian virus in China, see: Fang, L.-Q., Li, X.-L., Liu, K., Li, Y.-J., Yao, H.-W., Liang, S., Yang, Y., Feng, Z.-J., Gray, G.C., Cao, W.-C., 2013. Mapping Spread and Risk of Avian Influenza A (H7N9) in China. Sci Rep 3, 2722. https://doi.org/10.1038/srep02722.
 For more on forecasting spread and mapping in real-time outbreaks and epidemics, see: Desai, A.N., Kraemer, M.U.G., Bhatia, S., Cori, A., Nouvellet, P., Herringer, M., Cohn, E.L., Carrion, M., Brownstein, J.S., Madoff, L.C., Lassmann, B., 2019. Real-time Epidemic Forecasting: Challenges and Opportunities. Health Security 17, 268–275. https://doi.org/10.1089/hs.2019.0022.
 For more on the statistical tool that can estimate using healthcare maps, including ProMED and HealthMap, see: Bhatia, S., Lassmann, B., Cohn, E., Carrion, M., Kraemer, M.U.G., Herringer, M., Brownstein, J., Madoff, L., Cori, A., Nouvellet, P., 2019. Using Digital Surveillance Tools for Near Real-Time Mapping of the Risk of International Infectious Disease Spread: Ebola as a Case Study (preprint). Infectious Diseases (except HIV/AIDS). https://doi.org/10.1101/19011940.
 For more on new forecasting tools for estimating spread and jump of virusus from animals to humans, including virus mutation, see: Kraemer, M.U.G., Cummings, D.A.T., Funk, S., Reiner, R.C., Faria, N.R., Pybus, O.G., Cauchemez, S., 2019. Reconstruction and prediction of viral disease epidemics. Epidemiol. Infect. 147, e34. https://doi.org/10.1017/S0950268818002881