Addressing the COVID-19 transmission in inner Brazil by a mathematical model

AbstractIn 2020, the world experienced its very first pandemic of the globalized era. A novel coronavirus, SARS-CoV-2, is the causative agent of severe pneumonia and has rapidly spread through many nations, crashing health systems and leading a large number of people to death. In Brazil, the emergence of local epidemics in major metropolitan areas has always been a concern. In a vast and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for inner Brazil, and what can we do to control infection transmission in each of these locations? Here, a mathematical model is proposed to simulate disease transmission among individuals in several scenarios, differing by abiotic factors, social-economic factors, and effectiveness of mitigation strategies. The disease control relies on keeping all individuals’ social distancing and detecting, followed by isolating, infected ones. The model reinforces social distancing as the most efficient method to control disease transmission. Moreover, it also shows that improving the detection and isolation of infected individuals can loosen this mitigation strategy. Finally, the effectiveness of control may be different across the country, and understanding it can help set up public health strategies.

Download Full-text

Several countries in one: a mathematical modeling analysis for COVID-19 in inner Brazil

10.1101/2020.04.23.20077438 ◽

2020 ◽

Cited By ~ 1

Author(s):

G.B. de Almeida ◽

T.N. Vilches ◽

C.P. Ferreira ◽

C.M.C.B. Fortaleza

Keyword(s):

Disease Transmission ◽

Severe Pneumonia ◽

Mitigation Strategies ◽

Control Measures ◽

Control Effort ◽

Social Distancing ◽

Modeling Analysis ◽

The Social ◽

Control Disease ◽

Novel Coronavirus

ABSTRACTEarly 2020 and the world experiences its very first pandemic of globalized era. A novel coronavirus, SARS-Cov-2, is the causative agent of severe pneumonia and rapidly spread through many nations, crashing health systems. In Brazil, the emergence of local epidemics in major metropolitan areas is a concern. In a huge and heterogeneous country, with regional disparities and climate diversity, several factors can modulate the dynamics of COVID-19. What should be the scenario for an inner Brazil and what can we do to control infection transmission in each one of these locations? In this paper, a mathematical model was developed to simulate disease transmission among individuals in several scenarios, differing by the intensity and type of control measures. Mitigation strategies rely on social distancing of all individuals, and detection and isolation of infected ones. The model shows that control effort varies among cities. The social distancing is the most efficient method to control disease transmission but improving detection and isolation of infected individuals can help loosening this mitigation strategy.

Download Full-text

The SIR dynamic model of infectious disease transmission and its analogy with chemical kinetics

10.26434/chemrxiv.12021342.v3 ◽

2020 ◽

Author(s):

Cory Simon

Keyword(s):

Infectious Disease ◽

Mathematical Model ◽

Dynamic Model ◽

Chemical Kinetics ◽

Disease Transmission ◽

Dynamic Models ◽

Catalyst Deactivation ◽

Batch Reactor ◽

Mitigation Strategies ◽

Infectious Disease Transmission

The classic Susceptible-Infectious-Recovered (SIR) mathematical model of the dynamics of infectious disease transmission resembles a dynamic model of a batch reactor carrying out an auto-catalytic reaction with catalyst deactivation. By making this analogy between disease transmission and chemical reactions, chemists and chemical engineers can peer into dynamic models of infectious disease transmission used to forecast epidemics and assess mitigation strategies.

Download Full-text

DESIGN OF A NONLINEAR SITR FRACTAL MODEL BASED ON THE DYNAMICS OF A NOVEL CORONAVIRUS (COVID-19)

Fractals ◽

10.1142/s0218348x20400265 ◽

2020 ◽

Vol 28 (08) ◽

pp. 2040026 ◽

Cited By ~ 5

Author(s):

YOLANDA GUERRERO SÁNCHEZ ◽

ZULQURNAIN SABIR ◽

JUAN L. G. GUIRAO

Keyword(s):

Mathematical Model ◽

Fractal Model ◽

Contact Rate ◽

The Novel ◽

Nonlinear Mathematical Model ◽

Huge Number ◽

Social Distancing ◽

Natural Stabilization ◽

Novel Coronavirus ◽

The Mathematical Model

The aim of the present paper is to state a simplified nonlinear mathematical model to describe the dynamics of the novel coronavirus (COVID-19). The design of the mathematical model is described in terms of four categories susceptible ([Formula: see text], infected ([Formula: see text], treatment ([Formula: see text] and recovered ([Formula: see text], i.e. SITR model with fractals parameters. These days there are big controversy on if is needed to apply confinement measure to the population of the word or if the infection must develop a natural stabilization sharing with it our normal life (like USA or Brazil administrations claim). The aim of our study is to present different scenarios where we draw the evolution of the model in four different cases depending on the contact rate between people. We show that if no confinement rules are applied the stabilization of the infection arrives around 300 days affecting a huge number of population. On the contrary with a contact rate small, due to confinement and social distancing rules, the stabilization of the infection is reached earlier.

Download Full-text

Age-structured Impact of Mitigation Strategies on COVID-19 Severity and Deaths in Kenya

10.21203/rs.3.rs-105797/v1 ◽

2020 ◽

Author(s):

Samuel Mwalili ◽

Mark E. M. Kimathi ◽

Viona N. Ojiambo ◽

Duncan K. Gathungu ◽

Thomas N. O. Achia

Keyword(s):

School Closure ◽

Mitigation Strategies ◽

Global Perspective ◽

Cumulative Number ◽

Social Distancing ◽

First Case ◽

Age Structured ◽

October 17 ◽

Novel Coronavirus ◽

Age Structured Model

Abstract Introduction: COVID-19, a coronavirus disease 2019, is an ongoing pandemic caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). There have been a lot of attempts to model this pandemic from a global perspective. The Novel Coronavirus is still spreading quickly in several countries and the peak has not yet been reached in many countries. We developed age-structured model for describing the COVID-19 pandemic in Kenya under different non-pharmaceutical interventions. The first case in Kenya was identified in March 13, 2020 with the pandemic increasing to 465 confirmed cases by end of 3rd May, 2020. We fitted an age-structured deterministic mathematical model in Kenyan context.Methods: We model the COVID-19 situation in Kenya using Age-structured Susceptible Exposed Infectious Recovered compartmental model. These compartments follow a cascade of the disease from the Susceptible to Exposed individuals who in return are either symptomatic or asymptomatic. The symptomatic depict mild signs, which can develop to severe symptoms warranting hospitalization or can otherwise recover. The severe cases can recover with some developing critical condition. The critical are admitted at intensive care units. The resulting age-dependent ordinary differential equations from the model are solved using fourth order Runge-Kutta methods. We controlled for school closure, social distancing and lockdown in terms of movement restrictionsResults: The model shows varying epidemic peak by age-structure and the mitigation scenarios. The peak dates for unmitigated (UM), the 45% NPI (M45) and School closure-curfew-partial lockdown NPI (SCL) are May 21st, October 17th and December 13th 2020, respectively. Their respective cumulative infections peaks are 43M, 24M and 25M. The daily reported severe cases, critical cases and death proportionately increased with age. Conclusions: The cumulative number of infections reduces greatly with introduction of school closure, social distancing and restricted movement in highly affected counties. The degree of COVID-19 severity increases with age. However, it is not immediately clear when these restrictions can be lifted.

Download Full-text

Reduced social distancing during the COVID-19 pandemic is associated with antisocial behaviors in an online United States sample

10.31234/osf.io/ezypg ◽

2020 ◽

Cited By ~ 1

Author(s):

Katherine O'Connell ◽

Kathryn Berluti ◽

Shawn A Rhoads ◽

Abigail Marsh

Keyword(s):

Health Risks ◽

Disease Transmission ◽

Specific Form ◽

Protective Equipment ◽

The Novel ◽

Antisocial Behaviors ◽

Social Distancing ◽

Increase Risk ◽

Risk Of Disease ◽

Novel Coronavirus

Antisocial behaviors cause harm, directly or indirectly, to others’ welfare. The novel coronavirus pandemic has increased the urgency of understanding a specific form of antisociality: behaviors that increase risk of disease transmission. Because disease transmission-linked behaviors tend to be interpreted and responded to differently than other antisocial behaviors, it is unclear whether general indices of antisociality predict contamination-relevant behaviors. In a preregistered study using an online U.S. sample we found that individuals reporting high levels of antisociality engage in fewer social distancing measures: they report leaving their homes more frequently (p=.016, n=117) and standing closer to others while outside (p<.001, n=114). These relationships were observed after controlling for sociodemographic variables, illness risk, and use of protective equipment. Antisociality was not significantly associated with level of worry about the coronavirus. These findings suggest that more antisocial individuals may pose health risks to themselves and their community during the COVID-19 pandemic.

Download Full-text

Monitoring Disease Transmissibility of 2019 Novel Coronavirus Disease in Zhejiang, China

10.1101/2020.03.02.20028704 ◽

2020 ◽

Cited By ~ 1

Author(s):

Ka Chun Chong ◽

Wei Cheng ◽

Shi Zhao ◽

Feng Ling ◽

Kirran N. Mohammad ◽

...

Keyword(s):

Disease Transmission ◽

Social Distancing ◽

Reproduction Numbers ◽

Imported Cases ◽

Novel Coronavirus

AbstractWe monitored the transmissibility of 2019 novel coronavirus disease in Zhejiang accounting the transmissions from imported cases. Even though Zhejiang is one of the worst-affected provinces, an interruption of disease transmission (i.e. instantaneous reproduction numbers <1) was observed in early/mid-February after an early social-distancing response to the outbreak.

Download Full-text

Minimizing the infected peak utilizing a single lockdown: a technical result regarding equal peaks

10.1101/2021.06.26.21259589 ◽

2021 ◽

Author(s):

James Greene ◽

Eduardo D Sontag

Keyword(s):

Disease Transmission ◽

Disease Spread ◽

Optimal Timing ◽

The Novel ◽

Initiation Time ◽

Social Distancing ◽

Current State ◽

Optimal Protocol ◽

Common Criterion ◽

Novel Coronavirus

Due to the usage of social distancing as a means to control the spread of the novel coronavirus disease COVID-19, there has been a large amount of research into the dynamics of epidemiological models with time-varying transmission rates. Such studies attempt to capture population responses to differing levels of social distancing, and are used for designing policies which both inhibit disease spread but also allow for limited economic activity. One common criterion utilized for the recent pandemic is the peak of the infected population, a measure of the strain placed upon the health care system; protocols which reduce this peak are commonly said to `flatten the curve." In this work, we consider a very specialized distancing mandate, which consists of one period of fixed length of distancing, and addresses the question of optimal initiation time. We prove rigorously that this time is characterized by an equal peaks phenomenon: the optimal protocol will experience a rebound in the infected peak after distancing is relaxed, which is equal in size to the peak when distancing is commenced. In the case of a non-perfect lockdown (i.e. disease transmission is not completely suppressed), explicit formulas for the initiation time cannot be computed, but implicit relations are provided which can be pre-computed given the current state of the epidemic. Expected extensions to more general distancing policies are also hypothesized, which suggest designs for the optimal timing of non-overlapping lockdowns.

Download Full-text

Transmission of Respiratory Infectious Diseases between Neighboring Cities using Agent-based Model and Compartmental Model

10.1101/2020.06.24.20138818 ◽

2020 ◽

Author(s):

Christian Alvin H. Buhat ◽

Destiny SM. Lutero ◽

Yancee H. Olave ◽

Monica C. Torres ◽

Jomar F. Rabajante

Keyword(s):

Infectious Disease ◽

Disease Transmission ◽

Compartmental Model ◽

Preventive Measures ◽

Agent Based Model ◽

Agent Based ◽

Social Distancing ◽

Spatio Temporal ◽

Set Up ◽

Aid Decision

AbstractWe formulate an agent-based model and a compartmental model (SEIR) that simulate the spread of a respiratory infectious disease between two neighboring cities. We consider preventive measures such as implementation of social distancing and lockdown in a city, as well as the effect of protective gears or practices. The chance of travelling to another city and within the city during lockdown, and initial percentage of exposed and infected individuals on both cities influence the increase in the number of newly-infected individuals on both models. Our simulations show that (i) increase in exposed individuals results in increase in number of new infections, hence the need for increased testing-isolation efforts; (ii) protection level of 75-100% effectiveness impedes disease transmission; (iii) travelling within city or to other city can be an option given that strict preventive measures (e.g., non-pharmaceutical interventions) are observed; and (iv) the ideal set-up for neighboring cities is to implement lockdown when there is high risk of disease local transmission while individuals observe social distancing, maximizing protective measures, and isolating those that are exposed. The results of the agent-based and compartmental models show similar qualitative dynamics; the differences are due to different spatio-temporal heterogeneity and stochasticity. These models can aid decision makers in designing infectious disease-related policies to protect individuals while continuing population movement.

Download Full-text

Mathematical Model of COVID-19 Transmission Dynamics in South Korea: The Impacts of Travel Restrictions, Social Distancing, and Early Detection

Processes ◽

10.3390/pr8101304 ◽

2020 ◽

Vol 8 (10) ◽

pp. 1304

Author(s):

Byul Nim Kim ◽

Eunjung Kim ◽

Sunmi Lee ◽

Chunyoung Oh

Keyword(s):

Mathematical Model ◽

South Korea ◽

Spatial Heterogeneity ◽

Social Distancing ◽

Movement Data ◽

Reproduction Numbers ◽

Travel Restrictions ◽

Mobility Matrix ◽

Public Health Officials ◽

Novel Coronavirus

The novel coronavirus disease (COVID-19) poses a severe threat to public health officials all around the world. The early COVID-19 outbreak in South Korea displayed significant spatial heterogeneity. The number of confirmed cases increased rapidly in the Daegu and Gyeongbuk (epicenter), whereas the spread was much slower in the rest of Korea. A two-patch mathematical model with a mobility matrix is developed to capture this significant spatial heterogeneity of COVID-19 outbreaks from 18 February to 24 March 2020. The mobility matrix is taken from the movement data provided by the Korea Transport Institute (KOTI). Some of the essential patch-specific parameters are estimated through cumulative confirmed cases, including the transmission rates and the basic reproduction numbers (local and global). Our simulations show that travel restrictions between the epicenter and the rest of Korea effectively prevented massive outbreaks in the rest of Korea. Furthermore, we explore the effectiveness of several additional strategies for the mitigation and suppression of Covid-19 spread in Korea, such as implementing social distancing and early diagnostic interventions.

Download Full-text

Reduced social distancing early in the COVID-19 pandemic is associated with antisocial behaviors in an online United States sample

PLoS ONE ◽

10.1371/journal.pone.0244974 ◽

2021 ◽

Vol 16 (1) ◽

pp. e0244974

Author(s):

Katherine O’Connell ◽

Kathryn Berluti ◽

Shawn A. Rhoads ◽

Abigail A. Marsh

Keyword(s):

Disease Transmission ◽

Specific Form ◽

Protective Equipment ◽

The Novel ◽

Antisocial Behaviors ◽

Leaving Home ◽

Social Distancing ◽

Increase Risk ◽

Risk Of Disease ◽

Novel Coronavirus

Antisocial behaviors cause harm, directly or indirectly, to others’ welfare. The novel coronavirus pandemic has increased the urgency of understanding a specific form of antisociality: behaviors that increase risk of disease transmission. Because disease transmission-linked behaviors tend to be interpreted and responded to differently than other antisocial behaviors, it is unclear whether general indices of antisociality predict contamination-relevant behaviors. In a pre-registered study using an online U.S. sample, we found that individuals reporting high levels of antisociality engage in fewer social distancing measures: they report leaving their homes more frequently (p = .024) and standing closer to others while outside (p < .001). These relationships were observed after controlling for sociodemographic variables, illness risk, and use of protective equipment. Independently, higher education and leaving home for work were also associated with reduced distancing behavior. Antisociality was not significantly associated with level of worry about the coronavirus. These findings suggest that more antisocial individuals may pose health risks to themselves and their community during the COVID-19 pandemic.

Download Full-text