An epidemic model for COVID-19 transmission in Argentina: Exploration of the alternating quarantine and massive testing strategies

Abstract

The COVID-19 pandemic has challenged authorities at different levels of government administration around the globe. When faced with diseases of this severity, it is useful for the authorities to have prediction tools to estimate in advance the impact on the health system as well as the human, material, and economic resources that will be necessary. In this paper, we construct an extended Susceptible-Exposed-Infected-Recovered model that incorporates the social structure of Mar del Plata, the 4°most inhabited city in Argentina and head of the Municipality of General Pueyrredón. Moreover, we consider detailed partitions of infected individuals according to the illness severity, as well as data of local health resources, to bring predictions closer to the local reality. Tuning the corresponding epidemic parameters for COVID-19, we study an alternating quarantine strategy: a part of the population can circulate without restrictions at any time, while the rest is equally divided into two groups and goes on successive periods of normal activity and lockdown, each one with a duration of days. We also implement a random testing strategy with a threshold over the population. We found that is a good choice for the quarantine strategy since it reduces the infected population and, conveniently, it suits a weekly schedule. Focusing on the health system, projecting from the situation as of September 30, we foresee a difficulty to avoid saturation of the available ICU, given the extremely low levels of mobility that would be required. In the worst case, our model estimates that four thousand deaths would occur, of which 30% could be avoided with proper medical attention. Nonetheless, we found that aggressive testing would allow an increase in the percentage of people that can circulate without restrictions, and the medical facilities to deal with the additional critical patients would be relatively low.

Impact of individual behavior adoption heterogeneity on epidemic transmission in multiplex networks

“…[9] Based on these models, many scholars have studied the mechanisms underlying the spread of epidemics. [10][11][12][13][14] However, traditional studies of diffusion phenomena in single-layer networks often fail to provide a deeper understanding of the coupled effects on complex worlds. For example, epidemic transmission is often accompanied by epidemic-related information diffusion, and different information often affects the strength of the immunization measures in the face of the epidemic.…”

The relationship between controllability, optimal testing resource allocation, and incubation-latent period mismatch as revealed by COVID-19

“…However, overly simplistic models can yield erroneous conclusions regarding real-world control strategies, so one must carefully balance model simplicity against the complex realistic elements most relevant to the problem at hand. Conventional compartmental COVID-19 control models are typically based on systems of ordinary differential equations (ODE’s) (16, 17, 18, 19, 20, 21, 22, 23, 24, 25). While ODE disease models provide a level of mathematical tractability, they necessitate the coupling of symptom status to specific model compartments, and this structural constraint can result in unnatural or unrealistic representations of symptom onset and presymtomatic transmission with potential unintended consequences on model behavior and real-world interpretations.…”

“…One class of models simply ignores the potential for presymptomatic transmission by having infected individuals transition from an exposed non-symptomatic non-infectious compartment to an infectious symptomatic compartment, often with an additional infection channel comprised of permanently asymptomatic infected individuals. Such models have been used to analyze testing, contact tracing, and quarantine control strategies (16, 17), particularly in the context of limited resource constraints (18), along with vaccination control (19) and non-pharmaceutical interventions like masking and social distancing (20, 21). Although useful as simple baseline examples, these models may overestimate the efficacy of symptom-based COVID-19 controls due to the absence of presymptomatic transmission.…”

An approach based on process mining to assess the quarantine strategies' effect in reducing the COVID-19 spread

“…, 2021). Different statistical, simulation and data mining approaches (Vassallo et al. , 2022, Kuniya and Inaba, 2020, Kwan et al.…”

“…This phenomenon is of great concern its for efficiency and risks during long-term quarantine periods (Vasylieva et al, 2021). Different statistical, simulation and data mining approaches (Vassallo et al, 2022, Kuniya and Inaba, 2020, Kwan et al, 2021 have been published to estimate quarantine strategies and the majority signify the positive effects on managing this pandemic. Notably, the process perspective of COVID-19 datasets is of less concern among researchers (Chang et al, 2021).…”