We are witnessing an unprecedented event that is affecting all of us. COVID19 has impacted us in many different ways including our existence, ability to work, emotions, finances, freedom, societal interactions. The number of people who are testing positive for coronavirus is increasing exponentially on a daily basis and accordingly the number of people dying is also increasing. As of March 30, 2020, about 160,000 people in the United States have tested positive, about 3,000 died, and the unemployment rate by the summer of 2020 is projected to be as high as 32%. Any number we provide now, will almost surely will change within a day. These are very difficult times for all of us. It is a very serious situation that is affecting every country in terms of lives lost and lives affected. Although the popular buzz phrase today is social distancing to contain the disease exposure, as one of the chairs in our school noted, a more appropriate term might be physical distancing among people. We should continue to be socially connected via internet and other possible mediums but maintain physical distance. We all should avoid leaving homes unless we must. So, please stay home and be safe.
As biostatisticians in the School of Public Health, we have a major role to play in variety of ways. Below is a breakdown of some of the areas in which we can contribute.
A. Understanding heterogeneity in the population: As COVID19 data keeps pouring in, from a public health point of view, we need to understand how different (or heterogeneous) is the population. Are some groups of people more likely to test positive than others? Among those tested positive, are some more likely to die? Can we understand the heterogeneity in the temporal dynamics of the disease spread and death rates? By answering such questions we can (a) develop appropriate predictive models for different populations or subgroups and (b) mobilize resources, such as medical and others appropriately.
B. Modeling data under heterogeneity: As computational statisticians, one of our strengths is to develop and/or evaluate models. By understanding heterogeneity in populations, we can (a) try to evaluate the performance of the existing prediction models under a variety of scenarios, (b) if necessary, improve the models to account for heterogeneity, and (c) develop new stochastic models that are flexible and are capable of handling heterogeneity, such as hierarchical (empirical) Bayes models. Since the disease can potentially evolve at different rates among the heterogeneous populations, it will be important to develop dynamic models over time that account for heterogeneity in the populations. Social scientists are often interested in modeling networks in populations. Estimates of relationships among the nodes will be affected by the underlying heterogeneity in the population. Another important aspect of networks in the context of infectious diseases, which is often ignored, is that these networks are based on information from initial contacts leading “snowball sampling”, a term coined by Goodman (1961, Annals of Math. Statistics). Thus, there is a very rich structure to these networks that require to be modelled. Estimation of parameters under such structured networks requires special methods and standard methods may not be appropriate.
C. Financial toxicity: Estimation of financial toxicity is a complex problem. The socioeconomic impacts due to COVID19 is multi-layered and include (a) loss of household income due to unemployment & death of earning members, (b) temporary or permanent closure of small businesses, and (c) loss of productivity of a business. We have a variety of questions to address to fully examine these issues. For example, how should we conduct optimal survey designs for such studies? What are the suitable variables to investigate? How to model these resulting data? Studies such as these may have a major impact on healthcare, medicare, insurance companies etc.
D. Hospital management: Understanding the severity of disease will be critical when it comes to patient management in hospitals so that they can plan patient care suitably. This could potentially be a function of the heterogeneity in the populations noted in item A. Again, with suitable data, we can develop models and evaluate them to optimize the costs, however the term “cost” is defined.
E. Microbiology, molecular biology and disease level studies: A number of questions can be asked at the microbiome and virome level. How does COVID19 impact the gut microbiome of people? Are there any microbes that may serve a protective function against COVID19? Can the heterogeneity noted in (A) be partly explained by the gut microbiota? Thus, are some people more resistant to the disease than others due to a strengthened immune response associated with gut microbiome.
F. Group testing: As we are learning, screening individuals for COVID19 is a slow and expensive process. Many asymptomatic people in the population may be infected by the virus, and yet only people with symptoms might be screened, if at all. To obtain better prevalence estimates, which will be important develop accurate prediction models, one needs to screen large number of people in the population, not only those who show symptoms. This is a challenging problem. There are two aspects to the problem; (a) collecting biospecimens, which is a limited by the availability of kits – so I am not addressing that issue here! (b) Once a specimen is collected, the assaying process is also expensive and time consuming when dealing with large volume of people to screen. Statisticians have developed clever methods over the years called group testing methods. By understanding heterogeneity, noted in (A), one might develop novel approaches to group testing which can help better understand the prevalence rates of the virus.
There are numerous other questions that biostatisticians can engage in. While COVID19 is a horrible pandemic, this may not be the last one we shall experience in coming decades. We should develop tools from this tragedy for future use. Biostatisticians have a very special role to play in collaboration with researchers in various related subject matter fields. In some sense we can be in the center of inter-disciplinary research to answer various important questions.
Please feel free to contact us if you want to know more about our department. We look forward to meeting with you in the near future. In the meantime, stay home and stay safe.