Frequently Asked Question | No Public Health Intervention | Public Health Intervention |
---|---|---|

infectious contacts to 0 indays | ||

Days until peak of epidemic: | ||

Number of infected on peak day: | ||

Total deaths over 180 days: | ||

Estimated new infected as of | ||

Estimated new deaths as of |

The objective of this simulation is to illustrate and visualize the importance of public health interventions to decrease the current rate of infectiousness. In order to accomplish this objective, we illustrate two scenarios:

- Public Health Intervention Scenario
- No Public Health Intervention Scenario

The public health intervention scenario allows for the determination of the number of days it will take to change the latest available rate of infectiousness (R0) to a value of zero. Based on this public health intervention scenario, we provide an epidemiological prediction for the next 180 days.

The no public health intervention scenario holds constant the information for the latest available rate of infectiousness.

\begin{align} S & = \text{Susceptible population } \\ I & = \text{Infected}\\ D & = \text{Deaths } \\ R & = \text{Recovered} \\ \rho & = \text{infectiousness/communicability period} \\ \delta & = \text{The death rate} \\ t & = \text{Time period t} \\ \tau & = \text{The rate of transmission} \\ & = \frac{(I_{t} - I_{t-1})}{(SI)_{t-1}} \\ & = \frac{\Delta I_{t}}{(SI)_{t-1}} \end{align}

Defining the rate of infectiousness R0 on day zero as

\( RO = \frac{I_{t=0} - I_{t-1}}{I_{t-1}} \times \rho = \frac{\Delta I_{t=-1}}{I_{t=-1}} \times \rho \)

then the transmission rate on day zero can be alternatively written as

\( \tau_{t=0} = \frac{RO_{t=0}}{\rho S_{t-1}}\)

The public health intervention, which lets the user define the day on which R0 becomes zero, \( t_{RO=0}\), is linearly reduced as

\( \tau_{t} = \frac{RO_{t=0}}{\rho S_{t-1}} - \frac{\frac{RO_{t=0}}{\rho S_{t-1}}}{t_{RO=0}} \times t \ \forall \ t < t_{RO=0}, 0 \ otherwise\)

The upper and lower limit of the projections were calculated on the estimated transmission rates using the country specific data
for \(I_{t=-10} \) to \(I_{t=0} \), specifically

\(se(\tau)=\frac{\frac{\sum_{t=-10}^{0} (\tau_{t} - \bar{\tau})^2}{9}}{\sqrt{10}} \)

where

\( \bar{\tau} = \frac{\sum_{t=-10}^{0} \tau_{t}}{10}\)

Moreover, we assumed that the susceptible population during \( [-10 \leq t \leq 0]\) is equal to the respective country’s 2019 population
(Source: UN Population Prospects).

The upper and lower limits for \( \tau_{t=0}\) are then \( \tau_{t=0} \pm 1.96 \times se(\tau) \).

**Marcus Marktanner** is a Professor of Economics and International Conflict Management at Kennesaw State University (KSU).
He received his PhD from the Technical University of Ilmenau, Germany in 1997. Before joining KSU in 2011,
he held academic positions in Lebanon, the USA, and Germany. His research focuses on comparative economics, economic development,
conflict economics, and health economics. He has consulted various UN organizations and
regularly contributes to the work of the Konrad Adenauer Foundation as an author and speaker on the topic of the social market economy.

**Almuth D. Merkel** is a PhD student of International Conflict Management at Kennesaw State University.
She holds a Bachelor's degree in Ecotrophology and a Master's degree in Food and Agribusiness, both from Anhalt University of Applied Sciences, Germany.
Her research focuses on the economic causes and consequences of conflicts in general.
She has a particular interest in health economics, conflict economics, and the concept of the social market economy as a peacebuilding formula.
Her research is applied in nature and data-driven, including concepts of data visualization, economic impact studies, public policy simulations,
policy programming, and country-risk assessments.

For feedback of any kind or interest in collaboration, please email us at mmarktan@kennesaw.edu .