The Impact of COVID-19 Management Policies Tailored to Airborne SARS-CoV-2 Transmission: Policy Analysis

Abstract

Background Daily new COVID-19 cases from January to April 2020 demonstrate varying patterns of SARS-CoV-2 transmission across different geographical regions. Constant infection rates were observed in some countries, whereas China and South Korea had a very low number of daily new cases. In fact, China and South Korea successfully and quickly flattened their COVID-19 curve. To understand why this was the case, this paper investigated possible aerosol-forming patterns in the atmosphere and their relationship to the policy measures adopted by select countries. Objective The main research objective was to compare the outcomes of policies adopted by countries between January and April 2020. Policies included physical distancing measures that in some cases were associated with mask use and city disinfection. We investigated whether the type of social distancing framework adopted by some countries (ie, without mask use and city disinfection) led to the continual dissemination of SARS-CoV-2 (daily new cases) in the community during the study period. Methods We examined the policies used as a preventive framework for virus community transmission in some countries and compared them to the policies adopted by China and South Korea. Countries that used a policy of social distancing by 1-2 m were divided into two groups. The first group consisted of countries that implemented social distancing (1-2 m) only, and the second comprised China and South Korea, which implemented distancing with additional transmission/isolation measures using masks and city disinfection. Global daily case maps from Johns Hopkins University were used to provide time-series data for the analysis. Results The results showed that virus transmission was reduced due to policies affecting SARS-CoV-2 propagation over time. Remarkably, China and South Korea obtained substantially better results than other countries at the beginning of the epidemic due to their adoption of social distancing (1-2 m) with the additional use of masks and sanitization (city disinfection). These measures proved to be effective due to the atmosphere carrier potential of SARS-CoV-2 transmission. Conclusions Our findings confirm that social distancing by 1-2 m with mask use and city disinfection yields positive outcomes. These strategies should be incorporated into prevention and control policies and be adopted both globally and by individuals as a method to fight the COVID-19 pandemic.

SARS-COV-2: SIR Model Limitations and Predictive Constraints

“…The asymptotic feature of the phenomena relies on how virus transmission can be associated with a mixture of variables that sustain an indeterminate pattern of growing or reduction among countries. Worldwide, countries are facing daily new COVID-19 cases and the reason for countries to reduce its dissemination patterns are caused mainly due to HPALE on population [2][3][4][5][6][7][8] than a well-defined Earth seasonal period of COVID-19 transmission, as it is known that individual behavior and government policies are a major determinant for the pandemic peak reduction. This overall pandemic scenario could be observed in late March and starting in April 2020 when China and South Korea were the unique countries with the lowest rates of exponential growth of infection cases due to the type and strength of adopted HPALE [3,6], while Europe was in its fully active growing pattern.…”

“…However, this does not mean that environmental variables such as atmosphere properties or Earth seasonality do not present causation of the event. It implies that HPALE influences the phenomenon at its beginning and end with a persistent pattern [2][3][4][5][6][7][8] rather than what was expected to be addressed only by the environmental factors as the main driving force of seasonality during winter periods. For this reason, constant COVID-19 dissemination is expected during all Earth seasons and HPALE can be one of the main seasonality driven force observed worldwide.…”

“…However, despite this scheme pointing to the weaker Earth seasonality forcing behavior of SARS-COV-2 dissemination patterns, it can still influence the overall hidden transmission patterns due to HPALE interventions, environmentally driven seasonality, and urban spaces. This point can be addressed as a pattern formation ε , of each sample, of confounding forced seasonality that dismantles S and R compartments of SIR predictive model over time [1,[27][28][29][30][31][32][33][34], caused by environmental driven factors, urban spaces [35][36][37], and health HPALE intervention [2][3][4][5][6][7][8]. Then, it is possible to observe that each country might respond differently to the same initial conditions [8], influenced by the three components mentioned above, thus generating multiple patterns formation over time T for SARS-CoV-2 forms of transmission and periodicity.…”

“…This new dissemination pattern formation of the epidemic behavior was also described by Duarte et al [39] when the contact rate does not encompass weather conditions and time-varying aspects of epidemics. Therefore, an unpredictable shape k of probabilistic outcomes (close to reality shapes) was used, mainly defining this shape caused λ and ω asymptotic instabilities generated by S and R compartments over time [1,[27][28][29][30][31][32][33][34], among the environmental-and urban space-driven factors [35][36][37] and HPALE interventions [2][3][4][5][6][7][8]. This equation represents the presence of confounding and heterogeneous environmental variables ω with an unknown predictive scale of expλ or maximum likelihood estimator for λ due to non-linear inputs for S and R compartments over time as a global proposition (urban spaces, HPALE, and environmental conditions influence), thus generating nonlinear outputs k (asymptotic instability) [40,41].…”

“…This research's main focus is to point, as noted in Grassly and Fraser [1], to the consequences of seasonality for endemic R 0 stability in order to understand and obtain an endemic equilibrium for coronavirus disease 2019 (COVID- 19) involving mixing patterns such as environmental driving factors, policy interventions, and urban spaces [2][3][4][5][6][7][8]. These latter three variables might pose challenges for the outcomes of the SIR (susceptible, infectious, or recovered) predictive analysis [9] of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) spreading and dissemination patterns.…”