1165654 - Modeling Pandemic Health Data

The U.S. as well as the entire world was changed forever with the arrival of COVID-19. It has caused the worst health and economic disaster in modern times. Furthermore, its effects on health care, the economy, and the judicial system are certain to have far-reaching effects in both the near-term and in the future.
Pandemics in the past have repeatedly caught the technical community by surprise resulting in massive health problems that could justifiably be described as chaotic. Many now believe that the next pandemic will overwhelm all aspects of present-day medical systems and the guardians of public health almost certainly will once again be unprepared to provide and appropriate response.
This initial study presents a two-coefficient exponential model that may be employed to predict a host of health effects associated with viral-related diseases. These predictions can hopefully not only aide planning strategies but also “flatten” health impact curves earlier in a pandemic to reduce adverse effects associated with these diseases. The proposed mathematical model provides valuable medical-related information that can help guide public health scientists and managers confront these potential health problems.
COVID-19 data are extensive and continue to grow with time, and it is important to utilize these data to expedite the development of solutions that are specific to societal reopening and recovery. This can lead to tracking and reporting of both health and economic indicators that will allow awareness of and insight into how to move past a response phase and back into a “new normal.” Thus, it is especially important during the present COVID-19 or next pandemic to communicate meaningful and accurate up-to-the-moment information. This information cannot only provide to the technical community, but also to residents, and local officials insights needed to protect their communities and support informed and intelligent decision making.
It should be noted that the proposed equation presented in this paper describes global average health data variations and does not include positive spikes or negative valleys and other excursions from the trends indicated by the data. Including these variations from the trends would be nearly impossible to predict in a real-world scenario. Thus, derived models such as proposed in this paper should be employed only for estimation of the trend of disease transmission over time during a pandemic.
A careful examination of the results of this initial modeling effort applied the recent COVID-19 pandemic hospitalization and fatality data indicate that the proposed model does a reasonable job representing pandemic health data. It appears then, that this approach may therefore be used to describe trends in hospitalizations and fatalities for better management of disease outbreaks and pandemics in the future.