Mathematical model of COVID-19 intervention scenarios for São Paulo—Brazil

Neto, Osmar Pinto; Kennedy, Deanna M.; Reis, José Clark; Wang, Yiyu; Brizzi, Ana Carolina Brisola; Zambrano, Gustavo José; Souza, Joabe Marcos de; Pedroso, Wellington; Pedreiro, Rodrigo Cunha de Mello; Brizzi, Bruno de Matos; Abinader, Ellysson Oliveira; Zângaro, Renato Amaro

doi:10.1038/s41467-020-20687-y

Cited by 44 publications

(33 citation statements)

References 25 publications

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“…Compared to RL, evolutionary algorithms are easier to implement because the loss functions are easier to model and the algorithms are easy to train in parallel. Despite the fact that genetic algorithms (GA) are the most common evolutionary method (Pinto Neto et al, 2021; Miralles-Pechuán et al, 2020; Zhang G, 2021), we observed that they had high divergence caused by local optimums. Therefore, to obtain more reliable solutions, we decided to change our optimizer to Evolutionary Strategies (ES).…”

Section: Methodsmentioning

confidence: 74%

“…Pinto Neto et al (2021) used a multi-objective GA design optimization on a epidemiological compartmental model, named SUEIHCDR, to compare scenarios related to strategy type, the extent of social distancing, time window, and personal protection levels on the transmission dynamics of COVID-19 in São Paulo, Brazil. Their results indicated that the ability to reduce social distancing depends on a 5–10% increase in the current percentage of people strictly following protective guidelines, highlighting the importance of protective behavior in controlling the pandemic.…”

Section: Introductionmentioning

confidence: 99%

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Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Akinci

Fdez

Peña-Tapia

et al. 2021

Preprint

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In the context of the ongoing COVID-19 pandemic, while millions of people await the administration of a vaccine, social distancing remains the leading approach towards the effect commonly known as "flattening the curve" of infections. Over the last year, governmental administrations throughout the globe have implemented various lockdown policies in hopes of slowing down the transmission of the disease. However, the current lack of consensus on when and how these policies should be implemented reflects the need for further studies regarding these questions. In this paper, we tackle the issue of lockdown policy management, in particular in terms of lockdown placement (how often, when, and how long these periods should be), in order to minimize the peak of infections in a specific population. We introduce a novel combination of classic mathematical disease modelling using the equation-based SEIR model, and Evolutionary Strategies (ES) for optimizing the peak of infections. The method is evaluated using data collected in different countries, and a particular focus is placed on the study of the effect of specific model parameters on lockdown optimization, such as the transmission rate ($\beta$), of which 4 alternative modelling functions have been proposed and analyzed. Our results indicate that this transmission rate parameter significantly influences the resulting optimal strategies. In particular, the presence of a gradual decay of the rate of transmission during lockdown leads to longer, more sparsely placed confinement periods while an abrupt, instantaneous drop in the amount of contacts per person favors shorter but more frequent lockdowns. Although these results are limited by the scope of action provided by the simplicity of the SEIR model, they suggest that the influence of the evolution of the rate of transmission along the disease should be assessed in further studies with alternative optimization strategies (agent-based) and models (SEIRSHUD).

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Section: Methodsmentioning

confidence: 74%

Section: Introductionmentioning

confidence: 99%

Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Akinci

Fdez

Peña-Tapia

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Metaheuristic optimization approaches have also been applied to solve the optimal control strategies of the pandemic. These strategies range from the implementation of social distancing to reach herd immunity [163,164], to increasing testing and quarantine requirements [165], and to developing traditional Chinese medicine (TCM) prevention programs [166]. In [165], the authors developed a nature-inspired model to simulate the distribution process of COVID-19 in different countries and strive to maximize the number of "safe" countries (those that are immune to COVID-19).…”

Section: Prevention and Control: Curbing The Spread And Mitigating The Effectsmentioning

confidence: 99%

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

et al. 2021

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This paper presents an overview of some key results from a body of optimization studies that are specifically related to COVID-19, as reported in the literature during 2020-2021. As shown in this paper, optimization studies in the context of COVID-19 have been used for many aspects of the pandemic. From these studies, it is observed that since COVID-19 is a multifaceted problem, it cannot be studied from a single perspective or framework, and neither can the related optimization models. Four new and different frameworks are proposed that capture the essence of analyzing COVID-19 (or any pandemic for that matter) and the relevant optimization models. These are: (i) microscale vs. macroscale perspective; (ii) early stages vs. later stages perspective; (iii) aspects with direct vs. indirect relationship to COVID-19; and (iv) compartmentalized perspective. To limit the scope of the review, only optimization studies related to the prediction and control of COVID-19 are considered (public health focused), and which utilize formal optimization techniques or machine learning approaches. In this context and to the best of our knowledge, this survey paper is the first in the literature with a focus on the prediction and control related optimization studies. These studies include optimization of screening testing strategies, prediction, prevention and control, resource management, vaccination prioritization, and decision support tools. Upon reviewing the literature, this paper identifies current gaps and major challenges that hinder the closure of these gaps and provides some insights into future research directions.

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“…Mathematical models have been previously used with greater success in understanding the transmission dynamics and control mechanisms of infectious diseases [ 11 ]. To understand the early transmission dynamics of COVID-19 under different scenarios, a number of mathematical models have been previously proposed [ 12 – 21 ]. These established epidemiological and mathematical models provide important insights for public health decision-makers to enforce different mitigation strategies in different countries.…”

Section: Introductionmentioning

confidence: 99%

Assessing the impact of non-pharmaceutical interventions (NPI) on the dynamics of COVID-19: A mathematical modelling study of the case of Ethiopia

et al. 2021

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The World Health Organization (WHO) declared COVID-19 a pandemic on March 11, 2020 and by November 14, 2020 there were 53.3M confirmed cases and 1.3M reported deaths in the world. In the same period, Ethiopia reported 102K cases and 1.5K deaths. Effective public health preparedness and response to COVID-19 requires timely projections of the time and size of the peak of the outbreak. Currently, Ethiopia under the COVAX facility has begun vaccinating high risk populations but due to vaccine supply shortages and the absence of an effective treatment, the implementation of NPIs (non-pharmaceutical interventions), like hand washing, wearing face coverings or social distancing, still remain the most effective methods of controlling the pandemic as recommended by WHO. This study proposes a modified Susceptible Exposed Infected and Recovered (SEIR) model to predict the number of COVID-19 cases at different stages of the disease under the implementation of NPIs at different adherence levels in both urban and rural settings of Ethiopia. To estimate the number of cases and their peak time, 30 different scenarios were simulated. The results indicated that the peak time of the pandemic is different in urban and rural populations of Ethiopia. In the urban population, under moderate implementation of three NPIs the pandemic will be expected to reach its peak in December, 2020 with 147,972 cases, of which 18,100 are symptomatic and 957 will require admission to an Intensive Care Unit (ICU). Among the implemented NPIs, increasing the coverage of wearing masks by 10% could reduce the number of new cases on average by one-fifth in urban-populations. Varying the coverage of wearing masks in rural populations minimally reduces the number of cases. In conclusion, the models indicate that the projected number of hospital cases during the peak time is higher than the Ethiopian health system capacity. To contain symptomatic and ICU cases within the health system capacity, the government should pay attention to the strict implementation of the existing NPIs or impose additional public health measures.

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Mathematical model of COVID-19 intervention scenarios for São Paulo—Brazil

Cited by 44 publications

References 25 publications

Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Dynamic versus Continuous Interventions: Optimizing Lockdown Policies for COVID-19

Optimization in the Context of COVID-19 Prediction and Control: A Literature Review

Assessing the impact of non-pharmaceutical interventions (NPI) on the dynamics of COVID-19: A mathematical modelling study of the case of Ethiopia

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