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# COVIDHunter: An Accurate, Flexible, and Environment-Aware Open-Source
COVID-19 Outbreak Simulation Model

Subject Section

Mohammed Alser∗ Jeremie S. Kim  Nour Almadhoun Alserr  Stefan W. Tell  and
Onur Mutlu∗ ETH Zurich, Zurich 8006, Switzerland

(2019; 2019)

###### Abstract

Motivation: Early detection and isolation of COVID-19 patients are essential
for successful implementation of mitigation strategies and eventually curbing
the disease spread. With a limited number of daily COVID-19 tests performed in
every country, simulating the COVID-19 spread along with the potential effect
of each mitigation strategy currently remains one of the most effective ways
in managing the healthcare system and guiding policy-makers. We introduce
_COVIDHunter_ , a flexible and accurate COVID-19 outbreak simulation model
that evaluates the current mitigation measures that are applied to a region
and provides suggestions on what strength the upcoming mitigation measure
should be. The key idea of COVIDHunter is to quantify the spread of COVID-19
in a geographical region by simulating the average number of new infections
caused by an infected person considering the effect of external factors, such
as environmental conditions (e.g., climate, temperature, humidity) and
mitigation measures.
Results: Using Switzerland as a case study, COVIDHunter estimates that the
policy-makers need to keep the current mitigation measures for at least 30
days to prevent demand from quickly exceeding existing hospital capacity.
Relaxing the mitigation measures by 50% for 30 days increases both the _daily_
capacity need for hospital beds and _daily_ number of deaths exponentially by
an average of $23.8\times$, who may occupy ICU beds and ventilators for a
period of time. Unlike existing models, the COVIDHunter model accurately
monitors and predicts the daily number of cases, hospitalizations, and deaths
due to COVID-19. Our model is flexible to configure and simple to modify for
modeling different scenarios under different environmental conditions and
mitigation measures.
Availability: https://github.com/CMU-SAFARI/COVIDHunter
Contact: alserm<EMAIL_ADDRESS>omutlu<EMAIL_ADDRESS>
Supplementary information: Supplementary data is available at _Bioinformatics_
online.

##  1 Introduction

_Coronavirus disease 2019_ (COVID-19) is caused by SARS-CoV-2 virus, which was
first detected in Wuhan, the capital city of Hubei Province in China, in early
December 2019 (Du Toit, 2020). Since then, it has rapidly spread to nearly
every corner of the globe and has been declared a pandemic in March 2020 by
the World Health Organization (WHO). As of January 2021, COVID‑19 has since
resulted in more than 96 million laboratory-confirmed cases around the world,
and has killed nearly 2.2% of the infected population. As there are currently
no anti-SARS-CoV-2-specific drugs or effective vaccines widely available to
everyone, early detection and isolation of COVID-19 patients remain essential
for effectively curbing the disease spread. As a result, many countries across
the world have implemented unprecedented lockdown and social distancing
measures, affecting millions of people. Regardless of the availability and
affordability of COVID-19 testing, it is still extremely challenging to detect
and isolate COVID-19 infections at early stages due to three key issues. 1) It
is very difficult to accurately identify the initial contraction time of
COVID-19 for a patient. This is because COVID-19 patients can develop symptoms
between 2 to 14 days (or longer in a few cases) after exposure to the new
coronavirus (Lauer et al., 2020; Li et al., 2020). This variable delay is
referred to as the virus’ _incubation period_. 2) The coronavirus genome can
exhibit rapid genetic changes in its nucleotide sequence, which may occur
during viral cell replication, within the host body, or during transmission
between hosts (Andersen et al., 2020). This genetic diversity affects the
virus virulence, infectivity, transmissibility, and evasion of the host immune
responses (Phan, 2020; Pachetti et al., 2020; Toyoshima et al., 2020). 3) The
situation becomes even worse as the coronavirus can survive and therefore
remain _infectious_ outside the host, on common surfaces such as metal, glass,
and banknotes (both paper and polymer) at room temperature for _up to 28 days_
(Kampf et al., 2020; Riddell et al., 2020).

Simulating the spread of COVID-19 has the potential to mitigate the effects of
the three key issues, help to better manage the healthcare system, and provide
guidance to policy-makers on the effectiveness of various (current, planned or
discussed) social distancing and mitigation measures. To this end, many
COVID-19 simulation models are proposed (e.g., (Tradigo et al., 2020; Russell
et al., 2020; Ashcroft et al., 2020)), some of which are announced to _assist_
in decision-making for policy-makers in countries such as the United Kingdom
(ICL (Flaxman et al., 2020)), United States (IHME (Reiner et al., 2020)), and
Switzerland (IBZ (Huisman et al., 2020)). These models tend to follow one of
two key approaches. (1) Evaluating the current actual epidemiological
situation by accounting for reporting delays and under-reporting due to
inefficiencies such as low number of COVID-19 tests. (2) Evaluating the
current and future epidemiological situation by simulating the COVID-19
outbreak _without_ relying on the observed (laboratory-confirmed) number of
cases in simulation.

The first approach, taken by the IBZ (Huisman et al., 2020), LSHTM (Russell et
al., 2020), and (Ashcroft et al., 2020) models, is _not_ mainly used for
prediction purposes as it reflects the epidemiological situation with about
two weeks of time delay (due to its dependence on observed COVID-19 reports).
The IBZ model (Huisman et al., 2020) estimates the daily reproduction number,
$R$, of SARS-CoV-2 from observed COVID-19 incidence time series data after
accounting for reporting delays and under-reporting using the numbers of
confirmed hospitalizations and deaths. The $R$ number describes how a pathogen
spreads in a particular population by quantifying the average number of new
infections caused by each infected person at a given point in time. The LSHTM
model (Russell et al., 2020) adjusts the daily number of observed COVID-19
cases by accounting for under-reporting (uncertainty) using both deaths-to-
cases ratio estimates and correcting for delays between case confirmation
(i.e., laboratory-confirmed infection) to death.

The second approach, taken by ICL (Flaxman et al., 2020) and IHME (Reiner et
al., 2020) models, usually requires a large number of various input parameters
and assumptions. IHME (Reiner et al., 2020) model requires input parameters
such as testing rates, mobility, social distancing policies, population
density, altitude, smoking rates, self-reported contacts, and mask use. This
model makes two key assumptions: 1) the infection fatality rate (IFR), which
indicates the rate of people that die from the infection is taken using data
from the Diamond Princess Cruise ship and New Zealand and 2) the decreasing
fatality rate is reflective of increased testing rates (identifying higher
rates of asymptomatic cases). ICL (Flaxman et al., 2020) model requires input
parameters such as the daily number of confirmed deaths, IFR, mobility rates
from Google, age- and country-specific data on demographics, patterns of
social contact, and hospital availability. This model makes three key
assumptions: 1) age-specific IFRs observed in China and Europe are the same
across every country, 2) the number of confirmed deaths is equal to the true
number of COVID-19 deaths, and 3) the change in transmission rates is a
function of average mobility trends.

| Open | Well- | Accounting for | Low Number | Reported |
---|---|---|---|---|---|---
Model | Source | Documented# | Weather Changes | of Parameters | COVID-19 Statistics |
COVIDHunter (this work) | ✓ | ✓ | ✓ | ✓ |  ✓ ($R$, cases, hospitalizations, and deaths) |
IBZ (Huisman et al., 2020) | ✓ | ✗ | ✗ | ✓ |  ✗ (only $R$) |
LSHTM (Russell et al., 2020) | ✓ | ✗ | ✗ | ✓ | ✗ (only cases) |
ICL (Flaxman et al., 2020) | ✓ | ✓ | ✗ | ✗ |  ✓ ($R$, cases, hospitalizations, and deaths) |
IHME (Reiner et al., 2020) |  ✓∗ | ✗ | ✗ | ✗ | ✗ (cases, hospitalizations, and deaths) |

# Based on each model’s GitHub page (all models are available on GitHub). ∗
The available packages are configured _only_ for the IHME infrastructure.

Table 1: Comparison to other models used to inform government policymakers, as
of January 2021.

To our knowledge, there is currently no model capable of accurately monitoring
the current epidemiological situation and predicting future scenarios while
considering a reasonably low number of parameters and accounting for the
effects of environmental conditions, as we summarize in Table 1. The low
number of parameters provides four key advantages: 1) allowing flexible (easy-
to-adjust) configuration of the model input parameters for different scenarios
and different geographical regions, 2) enabling short simulation execution
time and simpler modeling, 3) enabling easy validation/correction of the model
prediction outcomes by adjusting fewer variables, and 4) being extremely
useful and powerful especially during the early stages of a pandemic as many
of the parameters are unknown. Simulation models need to consider the fact
that the environmental conditions (e.g., air temperature) affect pathogen
infectivity (Fares, 2013; Kampf et al., 2020; Riddell et al., 2020; Xu et al.,
2020) and simulating this effect helps to provide accurate estimation of the
epidemiological situation.

Our goal in this work is to develop such a COVID-19 outbreak simulation model.
To this end, we introduce _COVIDHunter_ , a simulation model that evaluates
the current mitigation measures (i.e., non-pharmaceutical intervention or NPI)
that are applied to a region and provides insight into what strength the
upcoming mitigation measure should be and for how long it should be applied,
while considering the potential effect of environmental conditions. Our model
accurately forecasts the numbers of infected and hospitalized patients, and
deaths for a given day, as validated on historical COVID-19 data (after
accounting for under-reporting). The key idea of COVIDHunter is to quantify
the spread of COVID-19 in a geographical region by calculating the daily
reproduction number, $R$, of COVID-19 and scaling the reproduction number
based on changes in both mitigation measures and environmental conditions. The
$R$ number changes during the course of the pandemic due to the change in the
ability of a pathogen to establish an infection during a season and mitigation
measures that lead to lower number of of susceptible individuals. COVIDHunter
simulates the entire population of a region and assigns _each_ individual in
the population to a stage of the COVID-19 infection (e.g., from being healthy
to being short-term immune to COVID-19) based on the scaled $R$ number. Our
model is flexible to configure and simple to modify for modeling different
scenarios as it uses _only_ three input parameters, two of which are time-
varying parameters, to calculate the $R$ number. Whenever applicable, we
compare the simulation output of our model to that of four state-of-the-art
models currently used to inform policy-makers, IBZ (Huisman et al., 2020),
LSHTM (Russell et al., 2020), ICL (Flaxman et al., 2020), and IHME (Reiner et
al., 2020).

The contributions of this paper are as follows:

  * •

We introduce COVIDHunter, a flexible and validated simulation model that
evaluates the current and future epidemiological situation by simulating the
COVID-19 outbreak. COVIDHunter accurately forecasts for a given day 1) the
reproduction number, 2) the number of infected people, 3) the number of
hospitalized people, 4) the number of deaths, and 5) number of individuals at
each stage of the COVID-19 infection. COVIDHunter evaluates the effect of
different current and future mitigation measures on the COVIDHunter’s five
numbers.

  * •

As a case study, we statistically analyze the relationship between temperature
and number of COVID-19 cases in Switzerland. We find that for each 1∘C rise in
daytime temperature, there is a 3.67% decrease in the daily number of
confirmed cases. We demonstrate how _considering_ the effect of climate (e.g.,
daytime temperature) on COVID-19 spread significantly improves the prediction
accuracy.

  * •

Compared to IBZ, LSHTM, ICL, and IHME models, COVIDHunter achieves more
accurate estimation, provides no prediction delay, and provides ease of use
and high flexibility due to the simple modeling approach that uses a small
number of parameters.

  * •

Using COVIDHunter, we demonstrate that the spread of COVID-19 in Switzerland
is still active (i.e., $R$ > 1.0) and curbing this spread requires maintaining
the same strength of the currently applied mitigation measures for at least
another 30 days.

  * •

We release the well-documented source code of COVIDHunter and show how easy it
is to flexibly configure for _any_ scenario and extend for different measures
and conditions than we account for.

##  2 Methods

###  2.1 Overview

The primary purpose of our COVIDHunter model is to _monitor and predict_ the
spread of COVID-19 in a flexibly-configurable and easy-to-use way, while
accounting for changes in mitigation measures and environmental conditions
over time. We employ a three-stage approach to develop and deploy this model.
(1) The COVIDHunter model predicts the daily $R$ value based on _only_ three
input parameters to maintain both quick simulation and high flexibility in
configuring these parameters. Each input parameter is configured based on
either existing research findings or user-defined values. Our model allows for
_directly_ leveraging existing models that study the effect of _only_
mitigation measures (or _only_ environmental conditions) on the spread of
COVID-19, as we show in Section 2.2. (2) The COVIDHunter model predicts the
number of COVID-19 cases based on the predicted $R$ number. COVIDHunter
simulates the entire population of a region and labels each individual
according to different stages of the COVID-19 infection timeline. Each stage
has a different degree of infectiousness and contagiousness. The model
simulates these stages for each individual to maintain accurate predictions.
(3) The COVIDHunter model predicts the number of hospitalizations and deaths
based on both the predicted number of cases and the $R$ number. Next, we
explain the COVIDHunter model in detail.

###  2.2 How does the COVIDHunter Model Work?

The COVIDHunter model predicts the dynamic value of $R$ for a population at a
given day while considering three key factors: 1) the transmissibility of an
infection into a susceptible host population, 2) mitigation measures (e.g.,
lockdown, social distancing, and isolating infected people), and 3)
environmental conditions (e.g., air temperature). Our model calculates the
time-varying $R$ number using Equation 1 as follows:

$R(t)=R0*(1-M(t))*C_{e}(t)$ (1)

The $R$ number for a given day, $t$, is calculated by multiplying three terms:
1) the base reproduction number ($R0$) for the subject virus, 2) one minus the
mitigation coefficient ($M$), for the given day $t$ and 3) the environmental
coefficient ($C_{e}$) for the given day $t$.

The $R0$ number quantifies the transmissibility of an infection into a
susceptible host population by calculating the expected average number of new
infections caused by an infected person in a population with no prior immunity
to a specific virus (as a pandemic virus is by definition novel to all
populations). Hence, the $R0$ number represents the transmissibility of an
infection at only the beginning of the outbreak assuming the population is not
protected via vaccination. Unlike the $R$ number, $R0$ number is a fixed value
and it does not depend on time. The $R$ number is a time-dependent variable
that accounts for the population’s reduced susceptibility. The $R0$ number for
the COVID-19 virus can be obtained from several existing studies (such as in
(Hilton and Keeling, 2020; Chang et al., 2020; Shi et al., 2020; de Souza et
al., 2020; Rahman et al., 2020)) that estimate it by modeling contact patterns
during the first wave of the pandemic.

The mitigation coefficient ($M$) applied to the population is a time-dependent
variable and it has a value between 0 and 1, where 1 represents the strongest
mitigation measure and 0 represents no mitigation measure applied. In
different countries, mitigation measures take different forms, such as social
distancing, self-isolation, school closure, banning public events, and
complete lockdown. These measures exhibit significant heterogeneity and differ
in timing and intensity across countries (Hale et al., 2020; Davies et al.,
2020). Quantifying the mitigation measures on a scale from 0 to 1 across
different countries is challenging. _The Oxford Stringency Index_ (Hale et
al., 2020) maintains a twice-weekly-updated index that takes values from 0 to
100, representing the severity of nine mitigation measures that are applied by
more than 160 countries. Another study (Brauner et al., 2020) estimates the
effect of _only_ seven mitigation measures on the $R$ number in 41 countries.
We can _directly_ leverage such studies for calculating the mitigation
coefficient on a given day after changing the scale from 0:100 to 0:1 by
dividing each value of, for example, the Oxford Stringency Index by 100.

The environmental coefficient ($C_{e}$) is a time-dependent variable
representing the effect of external environmental factors on the spread of
COVID-19 and it has a value between 0 and 2. Several related viral infections,
such as the Influenza virus, human coronavirus, and human respiratory, already
show notable seasonality (showing peak incidences during _only_ the winter (or
summer) months) (Moriyama et al., 2020; Fisman, 2012). The seasonal changes in
temperature, humidity, and ultraviolet light affect the pathogen
infectiousness outside the host (Fares, 2013; Kampf et al., 2020; Riddell et
al., 2020; Xu et al., 2020). However, the indoor environmental conditions are
usually well-controlled throughout the year, where human behavior and number
of households can be the major contributor to the spread of the COVID-19
(Moriyama et al., 2020). There are currently several studies that demonstrate
the strong dependence of the transmission of SARS-CoV-2 virus on one or more
environmental conditions, _even after_ controlling (isolating) the impact of
mitigation measures and behavioral changes that reduce contacts. Several
studies have demonstrated increased infectiousness by a country-dependent
fixed-rate with each 1∘C fall in daytime temperature (Xie and Zhu, 2020; Prata
et al., 2020). Another study supports the same temperature-infectiousness
relationship, but it also finds that before applying any mitigation measures,
a one degree drop in relative humidity shows increased infectiousness by a
rate lower (2.94$\times$ less) than that of temperature (Wang et al., 2020).

One of the most comprehensive studies that spans more than 3700 locations
around the world is _HARVARD CRW_ (Xu et al., 2020). It finds the statistical
correlation between the relative changes in the $R$ number and both weather
(temperature, ultraviolet index, humidity, air pressure, and precipitation)
and air pollution (SO2 and Ozone) after controlling the impact of mitigation
measures. The study provides a _CRW Index_ that has a value from 0.5 to 1.5.
The percentage difference between any two consecutive values provided by the
CRW Index represents the effect that both weather and air pollutants have on
the $R$ number. For example, a drop in the CRW Index by 10% in a given
location points to a 10% reduction in the $R$ number due to weather changes
and air pollutants. Our model enables applying _any of these studies_ by
adjusting our environmental coefficient on a given day, as we experimentally
demonstrate in Section 3. For example, if the COVIDHunter user chooses to
consider the HARVARD CRW study, and the CRW Index shows, for example, a 10%
drop compared to its immediately preceding data point, then the environmental
coefficient of COVIDHunter should be 0.9 so that the $R$ value decreases by
also 10%. Next, we explain how our model forecasts the number of COVID-19
cases based on Equation 1.

###  2.3 Predicting the Number of COVID-19 Cases

COVIDHunter tracks the number of infected and uninfected persons over time by
clustering the population into four main categories: HEALTHY, INFECTED,
CONTAGIOUS, and IMMUNE. The model initially considers the entire population as
uninfected (i.e., HEALTHY). For each simulated day, the model calculates the
$R$ value using Equation 1 and decides how many persons can be infected during
that day. The day when the first case of infection in a population introduced
is defined by the user. For each newly infected person (INFECTED), the model
maintains a counter that counts the number of days from being infected to
being contagious (CONTAGIOUS). Several COVID-19 case studies show that
_presymptomatic_ transmission can occur 1–3 days before symptom onset (Wei et
al., 2020; Slifka and Gao, 2020). COVID-19 patients can develop symptoms
mostly after an incubation period of 1 to 14 days (the median incubation
period is estimated to be 4.5 to 5.8 days) (Lauer et al., 2020; Li et al.,
2020). We calculate the number of days of being contagious after being
infected as a random number with a Gaussian distribution that has user-defined
lowest and highest values. Each contagious person may infect $N$ other persons
depending on mobility, population density, number of households, and several
other factors (Ferguson et al., 2020). We calculate the value of $N$ to be a
random number with a Gaussian distribution that has the lowest value of 0 and
the highest value determined by the user. If $N$ is greater than the $R$
number (i.e., the target number of infections for that day has been reached),
further infections are curtailed preventing overestimation of $N$ by infecting
_only_ $R$ persons. Once the contagious person infects the desired number of
susceptible persons, the status of the contagious person becomes immune
(IMMUNE). The immune status indicates that the person has immunity to
reinfection due to either vaccination or being recently infected (Lumley et
al., 2020).

Our model also simulates the effect of infected travelers (e.g., daily cross-
border commuters within the European Union) on the value of $R$. These
travelers can initiate the infection(s) at the beginning of the pandemic. If
such infected travelers are absent (due to, for example, emergency lockdown)
from the target population, the virus would die out once the value of $R$
decreases below 1 for a sufficient period of time. Both the number and
percentage of infected travelers entering a region are configurable in our
model. The percentage of incoming infected travelers is _not_ affected by the
changes in the local mitigation measures, as these travelers were infected
abroad.

Our model predicts the _daily_ number of COVID-19 cases for a given day $t$,
as follows:

$Daily\\_Cases(t)=\sum_{n=0}^{T_{INF}(t)}N(n)+\sum_{m=0}^{U_{CON}(t)}N(m)$ (2)

where $T_{INF}$ is the daily number of infected travelers that is a user-
defined variable, $N()$ is a function that calculates the number of persons to
be infected by a given person as a random number with a Gaussian distribution,
and $U_{CON}$ is the daily number of contagious persons calculated by our
model.

###  2.4 Predicting the Number of COVID-19 Hospitalizations and Deaths

There are currently two key approaches for calculating the estimated number of
both hospitalizations and deaths due to COVID-19: 1) using historical
statistical probabilities, each of which is unique to each age group in a
population (Bhatia and Klausner, 2020; Bi et al., 2020) and 2) using
historical COVID-19 hospitalizations-to-cases and deaths-to-cases ratios
(Kobayashi et al., 2020). We choose to follow a modified version of the second
approach as it does _not_ require 1) clustering the population into age-groups
and 2) calculating the risk of each individual using the given probability,
which both affect the complexity of the model and the simulation time.

The number of COVID-19 hospitalizations for a given day, $t$, can be
calculated as follows:

$Daily\\_Hospitalizations(t)=Daily\\_Cases(t)*X*C_{X}$ (3)

where $Daily\\_Cases(t)$ is calculated using Equation 2 and $X$ is the
hospitalizations-to-cases ratio that is calculated as the average of daily
ratios of the number of COVID-19 hospitalizations to the laboratory-confirmed
number of COVID-19 cases. As the _true_ number of cases is unknown due to lack
of population-scale testing, it is extremely difficult to make accurate
estimates of the _true_ number of COVID-19 hospitalizations. As such, we
assume a fixed multiplicative relationship between the number of laboratory-
confirmed cases and the _true_ number of cases. We use the user-defined
correction coefficient, $C_{X}$, of the hospitalizations-to-cases ratio to
account for such a multiplicative relationship.

The number of COVID-19 deaths for a given day $t$ can be calculated as
follows:

$Daily\\_Deaths(t)=Daily\\_Cases(t)*Y*C_{Y}$ (4)

where $Daily\\_Cases(t)$ is calculated using Equation 2 and $Y$ is the deaths-
to-cases ratio, which is calculated as the average of daily ratios of the
number of COVID-19 deaths to the number of COVID-19 laboratory-confirmed
cases. The observed number of COVID-19 deaths can still be less than the
_true_ number of COVID-19 deaths due to, for example, under-reporting. We use
the user-defined correction coefficient, $C_{Y}$, to account for the under-
reporting. One way to find the _true_ number of COVID-19 deaths is to
calculate the number of excess deaths. The number of excess deaths is the
difference between the observed number of deaths during time period and
expected (based on historical data) number of deaths during the same time
period. For this reason, $C_{Y}$ may not necessarily be equal to $C_{X}$.

###  2.5 Model Validation

We can validate our model using two key approaches. 1) Comparing the daily $R$
number predicted by our model (using Equation 1) with the daily reported
official $R$ number for the same region. 2) Comparing the daily number of
COVID-19 cases predicted by our model (using Equation 2) with the daily number
of laboratory-confirmed COVID-19 cases. As of January 2021, we have already
witnessed one year of the pandemic, which provides us several observations and
lessons. The most obvious source of uncertainty, affecting _all_ models, is
that the _true_ number of persons that are previously infected or currently
infected is _unknown_ (Wilke and Bergstrom, 2020). This affects the accuracy
of the reported $R$ number since it is calculated as, for example, the ratio
of the number of cases for a week (7-day rolling average) to the number of
cases for the preceding week. Adjusting the parameters of our model to fit the
curve of the number of confirmed cases is likely to be highly uncertain. The
publicly-available number of COVID-19 hospitalizations and deaths can provide
more reliable data.

For these reasons, we decide to use a combination of reported numbers of
cases, hospitalizations, and deaths for validating our model using three key
steps. 1) We leverage the more reliable data of reported number of
hospitalizations (or deaths) to estimate the _true_ number of COVID-19 cases
using the ratio of number of laboratory-confirmed hospitalizations (or deaths)
to the number of laboratory-confirmed cases during the second wave of the
COVID-19 pandemic. We assume that the COVID-19 statistics during the second
wave is more accurate than that during the first wave because generally more
testing is performed in the second wave. 2) We consider a multiplicative
relationship between the _true_ number of COVID-19 cases and that estimated in
step 1. In our experimental evaluation (Section 3), we use the _true_ number
of COVID-19 cases calculated using different multiplicative factor values (we
refer to them as _certainty rate levels_) as a ground-truth for validating our
model. A certainty rate of, for example, 50% means that the _true_ number of
COVID-19 cases is actually _double_ that calculated in step 1. 3) We use our
model to calculate both the daily $R$ number (using Equation 1) and the number
of COVID-19 cases (using Equation 2). We fix the two terms of Equation 1, $R0$
and $C_{e}$, using publicly-available data for a given region and change the
third term, $M$, until we fit the curve of the number of cases predicted by
our model to the ground-truth plot calculated in step 2. We use the same
methodology to validate our predicted numbers of hospitalizations and deaths
with different certainty rate levels as we show in Section 3 and the
Supplementary Excel File111https://github.com/CMU-
SAFARI/COVIDHunter/blob/main/Evaluation_Results/SimulationResultsForSwitzerland.xlsx.

###  2.6 Flexibility and Extensibility of the COVIDHunter Model

We especially build COVIDHunter model to be flexible to configure and easy to
extend for representing any existing or future scenario using different values
of the three terms of Equation 1, 1) $R0$, 2) $M(t)$, 3) $C_{e}(t)$, in
addition to several other parameters such as the population, number of
travelers, percentage of expected infected travelers to the total number of
travelers, and hospitalizations- or deaths-to-cases ratios. Our modeling
approach acts across the overall population without assuming any specific age
structure for transmission dynamics. It is still _possible_ to consider each
age group separately using individual runs of COVIDHunter model simulation,
each of which has its own parameter values adjusted for the target age group.
The COVIDHunter model considers each location independently of other
locations, but it also accounts for potential movement between locations by
adjusting the corresponding parameters for travelers. By allowing most of the
parameters to vary in time, $t$, the COVIDHunter model is capable of
accounting for any change in transmission intensity due to changes in
environmental conditions and mitigation measures over time. As we explain in
Section 2.2, the flexibility of configuring the environmental coefficient and
mitigation coefficient allows our proposed model to control for location-
specific differences in population density, cultural practices, age
distribution, and time-variant mitigation responses in each location. Our
modeling approach considers a single strain of the COVID-19 virus by using a
single base reproduction number, $R0$. It is possible to consider multiple
virus strains by running the model simulation multiple times, each of which
considers one of the strains individually. The model can be extended to
consider multiple virus strains by replacing the $R0$ number by multiple $R0$
numbers that represent the different strains (Reichmuth et al., 2021).

##  3 Results

We evaluate the daily 1) $R$ number, 2) mitigation measures, and 3) numbers of
COVID-19 cases, hospitalizations, deaths. We also evaluate the daily numbers
of HEALTHY, INFECTED, CONTAGIOUS, and IMMUNE in the Supplementary Excel
File222https://github.com/CMU-
SAFARI/COVIDHunter/blob/main/Evaluation_Results/SimulationResultsForSwitzerland.xlsx.
We compare the predicted values to their corresponding observed values
whenever possible. We provide a comprehensive treatment of _all_ datasets,
models, and evaluation results with different model configurations in the
Supplementary Materials and the Supplementary Excel
Files333https://github.com/CMU-
SAFARI/COVIDHunter/blob/main/Evaluation_Results/.

###  3.1 Determining the Value of Each Variable in the Equations

We use Switzerland as a use-case for all the experiments. However, our model
is not limited to any specific region as the parameters it uses are completely
configurable. To predict the $R$ number, we use Equation 1 that requires three
key variables. We set the base reproduction number, $R0$, for the SARS-CoV-2
in Switzerland as 2.7, as shown in (Hilton and Keeling, 2020). We choose two
main approaches for setting the value of the time-varying environmental
coefficient variable ($C_{e}$). 1) Performing statistical analysis for the
relationship between the daily number of COVID-19 cases and average daytime
temperature in Switzerland. As we provide in the Supplementary Materials,
Section 1, our statistical analysis shows that each 1∘C rise in daytime
temperature is associated with a 3.67% ($t$-value = -3.244 and $p$-value =
0.0013) decrease in the daily number of confirmed COVID-19 cases. We refer to
this approach as Cases-Temperature Coefficient (CTC). 2) Applying the _HARVARD
CRW_ (Xu et al., 2020) (CRW in short), which provides the statistical
relationship between the relative changes in the $R$ number and both weather
factors and air pollutants after controlling for the impact of mitigation
measures. We change the daily mitigation coefficient, $M(t)$, value based on
the ratio of number of confirmed hospitalizations to the number of confirmed
cases with two certainty rate levels of 100% and 50%, as we explain in detail
in Section 2.5. This helps us to take into account uncertainty in the observed
number of COVID-19 cases, hospitalizations, and deaths. We set the minimum and
maximum incubation time for SARS-CoV-2 as 1 and 5 days, respectively, as 5-day
period represents the median incubation period worldwide (Lauer et al., 2020;
Li et al., 2020). We set the population to 8654622. We empirically choose the
values of $N$, the number of travelers, and the ratio of the number of
infected travelers to the total number of travelers to be 25, 100, and 15%,
respectively.

###  3.2 Evaluating the Expected Number of COVID-19 Cases for Model Validation

As the exact true number of COVID-19 cases remains unknown (due to, for
example, lack of population-scale COVID-19 testing), we expect the true number
of COVID-19 cases in Switzerland to be higher than the observed (laboratory-
confirmed) number of cases. We calculate the expected true number of cases
based on both numbers of deaths and hospitalizations, as we explain in Section
2.5. To account for possible missing number of COVID-19 deaths, we consider
the excess deaths instead of observed deaths. We calculate the excess deaths
as the difference between the observed weekly number of deaths in 2020 and
5-year average of weekly deaths. We find that $X$ (hospitalizations-to-cases
ratio) and $Y$ (deaths-to-cases ratio, using excess death data) to be 3.526%
and 2.441%, respectively, during the second wave of the pandemic in
Switzerland. We choose the second wave to calculate the values of $X$ and $Y$
as Switzerland has increased the daily number of COVID-19 testing by
$5.31\times$ (21641/4074) on average compared to the first wave. We calculate
the expected number of cases on a given day $t$ with certainty rate levels of
100% and 50% based on hospitalizations by dividing the number of
hospitalizations at $t$ by $X$ and $X/2$, respectively, as we show in Figure
1. We apply the same approach to calculate the expected number of cases on a
given day $t$ with certainty rate levels of 100% and 50% based on deaths using
$Y$ and $Y/2$, respectively.

Based on Figure 1, we make two key observations. 1) The plot for the expected
number of cases calculated based on the number of deaths is shifted forward by
10-20 days (15 days on average) from that for the expected number of cases
calculated based on the number of hospitalizations. This is due to the fact
that each hospitalized patient usually spends some number of days in hospital
before dying of COVID-19. We do not observe a significant time shift between
the plot of the expected number of cases calculated based on the number
hospitalizations and the plot of observed (laboratory-confirmed) cases. 2) The
expected number of cases calculated based on the number of hospitalizations is
on average $1.99\times$ higher than the expected number of cases calculated
based on the number of deaths (after accounting for the 15-day shift) for the
same certainty rate. This is expected as not all hospitalized patients die.

We conclude that both numbers of hospitalizations and deaths can be used for
estimating the true number of COVID-19 cases after accounting for the time-
shift effect.

Figure 1: Observed (officially reported) and expected number of COVID-19 cases
in Switzerland during the year of 2020. We calculate the expected number of
cases based on both the hospitalizations-to-cases and deaths-to-cases ratios
for the second wave. We assume two certainty rate levels of 50% and 100%.

###  3.3 Observed and Predicted $R$ number of SARS-CoV-2

We calculate the predicted $R$ number using our model (Equation 1) and compare
it to the observed official $R$ number and the $R$ number of two state-of-the-
art models, ICL and IBZ, for the two years of 2020 and 2021. We configure
COVIDHunter using the following configurations: 1) CTC as environmental
condition approach, 2) certainty rate levels of 50% and 100%, and 3)
mitigation coefficient value of 0.7. All our scripts are provided in our
GitHub page. We consider the mean $R$ number provided by the ICL model. We
consider the median $R$ number calculated by the IBZ model based on observed
number of hospitalized patients. IBZ provides the predicted (after mid of
December 2020) $R$ number as the mean of the estimates from the last 7 days.

Based on Figure 2, we make three key observations. 1) COVIDHunter predicts the
changes in $R$ number much (4-13 days) earlier than that predicted by ICL
model, which leads to a more accurate prediction. The $R$ number predicted by
COVIDHunter (with a certainty rate level of 50%) is on average 1.56$\times$
less than that predicted by ICL model, IBZ model, and the observed official
$R$ number. Using a certainty rate level of 100%, COVIDHunter predicts the $R$
number to be close in value to the observed $R$ number. 2) Our model predicts
that the current $R$ number is still higher than 1 (1.137 and 1.023 using
certainty rate levels of 50% and 100%, respectively) during January 2021. This
indicates that the spread of the SARS-CoV-2 virus is still active and it
causes exponential increase in number of new cases. 3) Our model predicts that
if we keep the same mitigation measure strength as that of January 2021 for at
least 30 days (M(t)= 0.7), then the $R$ number would drop by 18.2% ($R$= 0.929
and 0.836 for certainty rate levels of 50% and 100%, respectively). However,
if the mitigation measures that are applied nationwide in Switzerland are
relaxed by 50% (M(t)= 0.35) for only 30 days (22 January to 22 February 2021),
then the $R$ number increases by at least 2.17$\times$.

We conclude that COVIDHunter’s estimation of the $R$ number is more accurate
than that calculated by the ICL and IBZ models, as validated by the currently
observed $R$ number.

Figure 2: Observed and predicted reproduction number, $R(t)$, for the two
years of 2020 and 2021. We use CTC environmental condition approach, certainty
rate levels of 50% and 100%, and mitigation coefficient values of 0.35 and 0.7
for COVIDHunter. We compare COVIDHunter’s predicted $R$ number to the observed
$R$ number and two state-of-the-art models, ICL and IBZ. The horizontal dashed
line represents $R(t)$ =1.0.

###  3.4 Evaluating the Mitigation Measures

We evaluate the mitigation coefficient, $M(t)$, which represents the
mitigation measures applied (or to be applied) in Switzerland from January
2020 to May 2021. We use two different environmental condition approaches, CRW
and CTC. We assume two certainty rate levels of 50% and 100% to account for
uncertainty in the observed number of cases. We use five mitigation
coefficients, $M(t)$, values of 0.35, 0.4, 0.5, 0.6, and 0.7 for each
configuration of COVIDHunter during 22 January to 22 February 2021. We compare
the evaluated mitigation measures to that evaluated by the Oxford Stringency
Index (Hale et al., 2020), as we provide in Figure 3. We also evaluate the
mitigation coefficient when we ignore the effect of environmental changes
(i.e., by setting $C_{e}$=1 in Equation 1), while maintaining the same number
of COVID-19 cases of that provided with a certainty rate level of 50%.

Based on Figure 3, we make four key observations. 1) Excluding the effect of
environmental changes from the COVIDHunter model, by setting $C_{e}$=1 in
Equation 1, leads to an inaccurate evaluation of the mitigation measures. For
example, during the summer of 2020 (between the two major waves of 2020),
COVIDHunter (WithoutCTC_50%) evaluates the mitigation coefficient to be as
high as 0.6. This means that the mitigation measures (_only_ mandatory of
wearing mask on public transport) applied during the summer of 2020 are _only_
14% more relaxed compared to the mitigation measures (e.g., closure of
schools, restaurants, and borders, ban on small and large events) applied
during the first wave, which is implausible. This highlights the importance of
considering the effect of external environmental changes on simulating the
spread of COVID-19. Unfortunately, environmental change effects are _not_
considered by _any_ of the IBZ, LSHTM, ICL, and IHME models, which we believe
is a serious shortcoming of these prior models. 2) A drop by 3% (as we observe
during the mid of November 2020) to 30% (as we observe during the end of
August 2020) in the strength of the mitigation measures for a certain period
of time (10 to 20 days) is enough to double the predicted number of COVID-19
cases. 3) We evaluate the strength of the mitigation measures applied in
Switzerland to be usually (65% of the time) up to 80% to 131% higher than that
provided by the Oxford Stringency Index. 4) The strength of the mitigation
measures has changed 11 times during the year of 2020, each of which is
maintained for at least 9 days and at most 66 days (32 days on average).

We conclude that considering the effect of environmental changes (e.g.,
daytime temperature) on the spread of COVID-19 improves simulation outcomes
and provides accurate evaluation of the strength of the past and current
mitigation measures.

Figure 3: Predicted strength of the mitigation measures (mitigation
coefficient, $M(t)$) applied in Switzerland from January 2020 to May 2021
provided by Oxford Stringency Index and COVIDHunter. We use two different
environmental condition approaches, CRW and CTC. We assume two certainty rate
levels of 50% and 100%. We use five mitigation $M(t)$ values of 0.35, 0.4,
0.5, 0.6, and 0.7 for each configuration of our model during 22 January to 22
February 2021. The plot called WithoutCTC_50% represents the evaluation of the
current mitigation measures while ignoring the effect of environmental
changes.

###  3.5 Evaluating the Predicted Number of COVID-19 Cases

We evaluate COVIDHunter’s _predicted_ daily number of COVID-19 cases in
Switzerland. We compare the predicted numbers by our model to the observed
numbers and those provided by three state-of-the-art models (ICL, IHME, and
LSHTM), as shown in Figure 4. We calculate the observed number of cases as the
expected number of cases with a certainty rate level of 100% (as we discuss in
Section 3.2). We use three default configurations for the prediction of the
ICL model: 1) strengthening mitigation measures by 50%, 2) maintaining the
same mitigation measures, and 3) relaxing mitigation measures by 50% which we
refer to as ICL+50%, ICL, and ICL-50%, respectively, in Figures 4, 5, and 6.
We use the mean numbers reported by the IHME model that represents the most
relaxed mitigation measures, called as "no vaccine" by the IHME model. We use
the median numbers reported by the LSHTM model.

Based on Figure 4, we make four key observations. 1) Our model predicts that
the number of COVID-19 cases reduces significantly (less than 600 daily cases)
within March 2021 if the same strength of the currently applied mitigation
measure is maintained for at least 30 days. If the authority decides to relax
the mitigation measures to the lowest strength that has been applied during
the year of 2020 (i.e., $M(t)=0.35$), then the daily expected number of cases
increases by an average of $29.6\times$ and $23.8\times$ (up to 288,827 daily
cases) using the CRW and CTC environmental approaches, respectively. We
provide a comprehensive evaluation for the effect of different mitigation
coefficient values on the number of cases in the Supplementary Materials,
Section 2. 2) COVIDHunter predicts the number of COVID-19 cases to be
equivalent to that predicted by the IHME model during the second wave with a
certainty rate level of 50%. However, during the first wave, the predictions
of the IHME model matches the expected number of cases using a certainty rate
level of 100%. This means that, unlike our model, the IHME model considers the
laboratory-confirmed cases to be as if the tests are done at a population-
scale during the first wave, which is very likely incorrect. This is in line
with a recent study (Ioannidis et al., 2020) that demonstrates the high
inaccuracy of the IHME model. 3) Overall, our model predicts on average
$1.7\times$ and $1.9\times$ smaller number of COVID-19 cases than that
predicted by ICL model using CTC and CRW approaches, respectively, and a
certainty rate of 50%. This suggests that the multiplicative relationship
between the confirmed number of cases and the true number of cases can be
represented by a certainty rate of 22% to 33%, which our model can easily
account for. The ICL model also shows that there is a sharp drop in the daily
number of cases after 13 November 2020, which corresponds to a 1.6$\times$,
1.4$\times$, and 1.3$\times$ increase in the Oxford Stringency Index, CRW
coefficient, and CTC coefficient, respectively, applied on 30 October 2020 as
we show in Figure 3. 4) The number of COVID-19 cases estimated by the LSHTM
model during the first wave is 1) on average 24% less than that estimated by
COVIDHunter and 2) 10 days late from that predicted by COVIDHunter, IHME, and
ICL. The prediction of the LSHTM model during the second wave is not available
by the model’s pre-computed projections.

We conclude that COVIDHunter provides more accurate estimation of the number
of COVID-19 cases, compared to IHME (which provides inaccurate estimation
during the first wave) and ICL (which provides over-estimation), with a
complete control over the certainty rate level, mitigation measures, and
environmental conditions. Unlike LSHTM, COVIDHunter also ensures no prediction
delay.

Figure 4: Observed and predicted number of COVID-19 cases by our model and
other three state-of-the-art models. We use two different environmental
condition approaches, CRW and CTC with two certainty rate levels of 50% and
100%. We use two mitigation coefficient, $M(t)$, values of 0.35 and 0.7 for
each configuration of our model during 22 January to 22 February 2021.

###  3.6 Evaluating the Predicted Number of COVID-19 Hospitalizations

We evaluate COVIDHunter’s _predicted_ daily number of COVID-19
hospitalizations in Figure 5. We use the observed official number of
hospitalizations as is. Using the number of cases calculated with Equation 2,
we find $X$ (hospitalizations-to-cases ratio) to be 4.288% and 2.780%, using
CRW and CTC, respectively, during the second wave.

We make five key observations based on Figure 5. 1) The number of
hospitalizations calculated by COVIDHunter with a certainty rate level of 50%
matches that calculated by the IHME model. However, IHME model provides a
10-12-day late prediction compared to that provided by COVIDHunter and the ICL
model. 2) The ICL model predicts the number of hospitalizations to be
$5\times$ and $7\times$ higher than that predicted by COVIDHunter during the
first wave ($9.3\times$ and $8.1\times$ during the second wave), using the CTC
and CRW approaches, respectively, for evaluating the environmental conditions
and a certainty rate of 50%. This suggests that the ICL model provides
$10\times$ and $18.6\times$ higher number of hospitalizations compared to the
observed number of hospitalizations, during first and second waves,
respectively, which is highly unlikely and overestimated. 3) COVIDHunter with
a certainty rate level of 100% predicts the number of cases to perfectly fit
the curve of the observed number of hospitalizations, reaching up to 257
hospitalized patients a day. 4) Our model predicts that the number of COVID-19
hospitalizations reduces with stricter mitigation measures maintained for at
least 30 days. Relaxing the mitigation measures by 50% ($M$ is changed from
0.7 to 0.35) _exponentially_ increases the number of hospitalizations by an
average of $29.6\times$ and $23.8\times$, reaching up to 12385 new daily
hospitalized patients, as predicted by COVIDHunter using CRW and CTC
environmental condition approaches, respectively. This is in line with what
the ICL model (ICL-50%) predicts, when ICL model is configured to 50%
relaxation in the mitigation measures. 5) The use of the CTC approach for
determining the environmental coefficient value yields a slightly different
number of hospitalizations compared to that provided by the use of the CRW
approach. This is expected as the CTC approach considers only the monthly
average change in temperature, whereas the CRW approach considers the daily
change in _several_ environmental conditions.

We conclude that 1) unlike the IBZ and LSHTM models, COVIDHunter is able to
predict the number of hospitalizations and 2) COVIDHunter provides more
accurate estimation of the number of hospitalizations compared to that
calculated by ICL (which provides overestimation) and IHME (which provides
late estimation). COVIDHunter predicts the number of COVID-19 hospitalizations
in a simple, convenient and flexible way that requires calculating only the
daily number of cases and the hospitalization-to-cases ratio, $C_{X}$.

Figure 5: Observed and predicted number of COVID-19 hospitalizations. We use
two different environmental condition approaches, CRW and CTC with two
certainty rate levels of 50% and 100%. We use two mitigation coefficient
values, $M(t)$, of 0.35 and 0.7 for each configuration of our model during 22
January to 22 February 2021.

###  3.7 Evaluating the Predicted Number of COVID-19 Deaths

We evaluate COVIDHunter’s _predicted_ daily number of COVID-19 deaths in
Figure 6 after accounting for the 15-day shift (as we discuss in Section 3.2).
We calculate the observed number of deaths as the number of excess deaths
(Section 2.4) to account for uncertainty in reporting COVID-19 deaths. Using
the number of cases calculated using Equation 2, we find $Y$ (deaths-to-cases
ratio, using excess death data) to be 2.730% and 1.739%, using CRW and CTC,
respectively, during the second wave.

We make three key observations based on Figure 6. 1) COVIDHunter with a
certainty rate of 100% predicts the number of deaths to perfectly fit the
three curves of the observed number of _excess_ deaths, ICL deaths, and IHME
deaths, reaching up to 160 hospitalized patients a day. During the second
wave, the ICL curve is shifted (late prediction) by 5-10 days from that of
other models. 2) Similar to what we observe for the number of
hospitalizations, our model predicts that the number of COVID-19 deaths
significantly reduces with stricter mitigation measures maintained for at
least the upcoming 30 days. Relaxing the mitigation measures by 50% ($M(t)$ is
changed from 0.7 to 0.35) _exponentially_ increases the death toll by an
average of $29.6\times$ and $23.8\times$, reaching up to 7885 new daily
deaths, as predicted by COVIDHunter using CRW and CTC environmental condition
approaches, respectively. 3) During the first wave, the use of a certainty
rate of 50% provides $2.55\times$ and $2.1\times$ ($2.36\times$ and
$1.52\times$ during the second wave) higher number of deaths compared to that
provided by ICL and IHME models, when COVIDHunter uses CRW and CTC
environmental condition approaches, respectively.

We conclude that 1) unlike the IBZ and LSHTM models, COVIDHunter is able to
predict the number of deaths, 2) COVIDHunter predicts the number of deaths to
be similar to that predicted by the ICL and IHME models. Yet, COVIDHunter
provides more accurate estimation of other COVID-19 statistics ($R$, number of
cases and hospitalizations) compared to ICL and IHME, as we comprehensively
evaluate in the previous sections, and 3) COVIDHunter requires calculating
only the daily number of cases and the deaths-to-cases ratio, $C_{Y}$, to
predict the daily number of deaths.

Figure 6: Observed and predicted number of COVID-19 deaths. We use two
different environmental condition approaches, CRW and CTC with two certainty
rate levels of 50% and 100%. We use two mitigation coefficient values, $M(t)$,
of 0.35 and 0.7 for each configuration of our model during 22 January to 22
February 2021.

##  4 Summary and Future Work

We demonstrate that we can monitor and predict the spread of COVID-19 in an
easy-to-use, flexible, and validated way using our new simulation model,
COVIDHunter. We show how to flexibly configure our model for any scenario and
easily extend it for different mitigation measures and environmental
conditions. The use of a small number of variables in our model enables a
simple and flexible yet powerful way of adapting our model to different
conditions for a given region. We demonstrate the importance of considering
the effect of environmental changes on the spread of COVID-19 and how doing so
can greatly improve simulation accuracy. COVIDHunter flexibly offers the
ability to directly make the best use of existing models that study the effect
of one or both of environmental conditions and mitigation measures on the
spread of COVID-19.

We benchmark our model against major alternative models of the COVID-19
pandemic that are used to assist governments. Compared to these models,
COVIDHunter achieves more accurate estimation, provides no prediction delay,
and provides ease of use and high flexibility due to the simple modeling
approach that uses a small number of parameters. Using COVIDHunter, we
demonstrate that the spread of COVID-19 in Switzerland (as a case study) is
still active (i.e., $R$ > 1.0) and curbing this spread requires maintaining
the same strength of the currently applied mitigation measures for at least
another 30 days. Using COVIDHunter (CTC_100%_M(t)=0.7) on 7 January 2021, we
predicted that on 27 January 2021 the number of cases, hospitalizations, and
deaths will drop by 19%, 20%, and 30%, respectively. The predicted drop is in
line with the observed official number of cases, hospitalizations, and deaths
(as shown by the Federal Office of Public Health in Switzerland
www.covid19.admin.ch) but with different ratios (41%, 59%, and 49%,
respectively). We believe the difference between the observed and the
COVIDHunter’s predicted numbers of cases, hospitalizations, and deaths is due
to one or more of the following reasons: 1) The lack of population-scale
COVID-19 testing, 2) the use of a more stricter mitigation measure than
$M(t)=0.7$, and 3) the lack of information about ground truth on number of
COVID-19 cases, hospitalizations, and deaths. We provide insights on the
effect of each change in the strength of the applied mitigation measure on the
number of daily cases, hospitalizations, and deaths. We make all the data,
statistical analyses, and a well-documented model implementation publicly and
freely available to enable full reproducibility and help society and decision-
makers to accurately and openly review the current situation and estimate
future impact of decisions.

We suggest and plan at least five main directions/additions to further improve
the predictive power and benefits of our COVIDHunter model. 1) Clustering the
population based on age-groups. This has potential different effects on, for
example, population, environmental conditions, mitigation measures (Bhatia and
Klausner, 2020; Bi et al., 2020). 2) Considering vaccinated persons as another
new category of persons in a population. 3) Considering reinfection after
immunity (Lumley et al., 2020). 4) Considering the average number of
households (or population density), as well as other potential population-
level effects, while calculating the number of new infected persons caused by
an infected person. 5) Considering different strains of the COVID-19 virus by
allowing for multiple base reproduction numbers. Our goal is to update
COVIDHunter with such improvements and capabilities while keeping its
simplicity, ease of use, and flexibility of its modeling strategy.

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