All-People-Test-Based Methods for COVID-19 Infectious Disease Dynamics Simulation Model: Towards Citywide COVID Testing

Abstract

The conversion rate between asymptomatic infections and reported/unreported symptomatic infections is a very sensitive parameter for model variables that spread COVID-19. This is important information for follow-up use in screening, prediction, prognostics, contact tracing, and drug development for the COVID-19 pandemic. The model described here suggests that there may not be enough researchers to solve all of these problems thoroughly and effectively, and it requires careful selection of what we are doing and rapid sharing of results and models and optimizing modeling simulations with value to reduce the impact of COVID-19. Exploring simulation modeling will help decision makers make the most informed decisions. In order to fight against the “Delta” virus, the establishment of a line of defense through all-people testing (APT) is not only an effective method summarized from past experience but also one of the best means to effectively cut the chain of epidemic transmission. The effect of large-scale testing has been fully verified in the international community. We developed a practical dynamic infectious disease model-SETPG (A + I) RD + APT by considering the effects of the all-people test (APT). The model is useful for studying effects of screening measures and providing a more realistic modelling with all-people-test strategies, which require everybody in a population to be tested for infection. In prior work, a total of 370 epidemic cases were collected. We collected three kinds of known cases: the cumulative number of daily incidences, daily cumulative recovery, and daily cumulative deaths in Hong Kong and the United States between 22 January 2020 and 13 November 2020 were simulated. In two essential strategies of the integrated SETPG (A + I) RD + APT model, comparing the cumulative number of screenings in derivative experiments based on daily detection capability and tracking system application rate, we evaluated the performance of the timespan required for the basic regeneration number (R0) and real-time regeneration number (R0t) to reach 1; the optimal policy of each experiment is available, and the screening effect is evaluated by screening performance indicators. with the binary encoding screening method, the number of screenings for the target population is 8667 in HK and 1,803,400 in the U.S., including 6067 asymptomatic cases in HK and 1,262,380 in the U.S. as well as 2599 cases of mild symptoms in HK and 541,020 in the U.S.; there were also 8.25 days of screening timespan in HK and 9.25 days of screening timespan required in the U.S. and a daily detectability of 625,000 cases in HK and 6,050,000 cases in the U.S. Using precise tracking technology, number of screenings for the target population is 6060 cases in HK and 1,766,420 cases in the U.S., including 4242 asymptomatic cases in HK and 1,236,494 cases in the U.S. as well as 1818 cases of mild symptoms in HK and 529,926 cases in the U.S. Total screening timespan (TS) is 8.25~9.25 days. According to the proposed infectious dynamics model that adapts to the all-people test, all of the epidemic cases were reported for fitting, and the result seemed more reasonable, and epidemic prediction became more accurate. It adapted to densely populated metropolises for APT on prevention.

Epi-DNNs: Epidemiological priors informed deep neural networks for modeling COVID-19 dynamics

“…took the conversion rate between asymptomatic infections and reported/unreported symptomatic infections into account, and proposed an infectious dynamics model that adapts to all-people testing (APT). It adapted to densely populated metropolises for APT on prevention, where the result seemed more reasonable, and epidemic prediction became more accurate [25] . The primary limitation of these compartmental models is that they are subject to assumptions about the transmission process and the parameters.…”

COVID-19 in Italy: Is the Mortality Analysis a Way to Estimate How the Epidemic Lasts?

“…Nowadays, several researchers are searching for antique and new tools in data analysis to modeling and forecasting studies applied in this pandemic. The epidemic analysis for forecasting COVID-19 using, among others, the susceptible, infected, and recovered (SIR) [ 2 ], susceptible, infected, recovered, and deceased (SIRD) [ 3 ], susceptible, exposed, infected, and recovered (SEIR) [ 4 ], susceptible, exposed, infected, recovered and dead (SEIRD) [ 5 ], SEIRD model with the compartment of vaccinated people (SEIRDV) [ 6 ], and Moving Average [ 7 ] models, or even hybrid dynamic model as is SEIRD with Automatic Regressive Integrated Moving Average (ARIMA) corrections [ 8 ] or data-driven hybrid technique by integrating an ensemble empirical mode decomposition (EEMD), an autoregressive integrated moving average (ARIMA), with a nonlinear autoregressive artificial neural network (NARANN), called the EEMD-ARIMA-NARANN model [ 9 ]. Predictive models of mortality have also been studied, highlighting the study of Friedman et al [ 10 ], which observed that seven COVID-19 models covered more than five countries, suggesting that effects of seasonality or continued slow [ 11 , 12 , 13 , 14 , 15 , 16 , 17 ] declines in mortality could be responsible for converging in their predictions for the June–August 2020 period.…”