scholarly journals Mathematical Parameters of the COVID-19 Epidemic in Brazil and Evaluation of the Impact of Different Public Health Measures

Biology ◽  
2020 ◽  
Vol 9 (8) ◽  
pp. 220 ◽  
Author(s):  
Renato M. Cotta ◽  
Carolina P. Naveira-Cotta ◽  
Pierre Magal
Keyword(s):  
Public Health ◽  
Initial Conditions ◽  
Transmission Rate ◽  
Time Variable ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Problem Model ◽  
Social Distancing ◽  
Health Measures ◽  
The Impact

A SIRU-type epidemic model is employed for the prediction of the COVID-19 epidemy evolution in Brazil, and analyze the influence of public health measures on simulating the control of this infectious disease. The proposed model allows for a time variable functional form of both the transmission rate and the fraction of asymptomatic infectious individuals that become reported symptomatic individuals, to reflect public health interventions, towards the epidemy control. An exponential analytical behavior for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for the estimation of parameters by employing the direct problem model with the data from the first phase of the epidemy evolution, represented by the time series for the reported cases of infected individuals. The evolution of the COVID-19 epidemy in China is considered for validation purposes, by taking the first part of the dataset of accumulated reported infectious individuals to estimate the related parameters, and retaining the rest of the evolution data for direct comparison with the predicted results. Then, the available data on reported cases in Brazil from 15 February until 29 March, is used for estimating parameters and then predicting the first phase of the epidemy evolution from these initial conditions. The data for the reported cases in Brazil from 30 March until 23 April are reserved for validation of the model. Then, public health interventions are simulated, aimed at evaluating the effects on the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviors for these two parameters. This first constructed model provides fairly accurate predictions up to day 65 below 5% relative deviation, when the data starts detaching from the theoretical curve. From the simulated public health intervention measures through five different scenarios, it was observed that a combination of careful control of the social distancing relaxation and improved sanitary habits, together with more intensive testing for isolation of symptomatic cases, is essential to achieve the overall control of the disease and avoid a second more strict social distancing intervention. Finally, the full dataset available by the completion of the present work is employed in redefining the model to yield updated epidemy evolution estimates.

scholarly journals Modelling the COVID-19 epidemics in Brasil: Parametric identification and public health measures influence

2020 ◽  
Author(s):  
R.M. Cotta ◽  
C.P. Naveira-Cotta ◽  
P. Magal
Keyword(s):  
Public Health ◽  
Initial Conditions ◽  
Transmission Rate ◽  
Parametric Identification ◽  
Parametric Estimation ◽  
Health Interventions ◽  
Problem Analysis ◽  
Public Health Interventions ◽  
Problem Model ◽  
Health Measures

ABSTRACTA SIRU-type epidemic model is proposed for the prediction of COVID-19 spreading within Brasil, and analyse the influence of public health measures on simulating the control of this infectious disease. Since the reported cases are typically only a fraction of the total number of the symptomatic infectious individuals, the model accounts for both reported and unreported cases. Also, the model allows for the time variation of both the transmission rate and the fraction of asymptomatic infectious that become reported symptomatic individuals, so as to reflect public health interventions, towards its control, along the course of the epidemic evolution. An analytical exponential behaviour for the accumulated reported cases evolution is assumed at the onset of the epidemy, for explicitly estimating initial conditions, while a Bayesian inference approach is adopted for parametric estimations employing the present direct problem model with the data from the known portion of the epidemics evolution, represented by the time series for the reported cases of infected individuals. The direct-inverse problem analysis is then employed with the actual data from China, with the first half been employed for the parametric estimation and the second half for validation of the predictive capability of the proposed approach. The full dataset for China is then employed in another parameter identification, aimed at refining the values for the average times that asymptomatic infectious individuals and that symptomatic individuals remain infectious. Following this validation, the available data on reported cases in Brasil from February 15th till March 29th, 2020, is used for estimating parameters and then predict the epidemy evolution under these conditions. Finally, public health interventions are simulated, aimed at diminishing the effects of the disease spreading, by acting on both the transmission rate and the fraction of the total number of the symptomatic infectious individuals, considering time variable exponential behaviours for these two parameters, usually assumed constant in epidemic evolutions without intervention. It is demonstrated that a combination of actions to affect both parameters can have a much faster and effective result in the control of the epidemy dynamics.

scholarly journals Reconceptualizing Social Distancing: Teletherapy and Social Inequality During the COVID-19 and Loneliness Pandemics

2020 ◽  
Vol 60 (5) ◽  
pp. 626-638 ◽  
Author(s):  
José G. Luiggi-Hernández ◽  
Andrés I. Rivera-Amador
Keyword(s):  
Public Health ◽  
Psychological Impact ◽  
Psychological Effects ◽  
Health Interventions ◽  
Negative Consequences ◽  
Cautionary Tale ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Health Measures ◽  
The Impact

The purpose of this article is to highlight the impact of the COVID-19 pandemic amid a preexisting loneliness epidemic, as well as argue in favor of the reconceptualization of social distancing as physical distancing. As public health measures require us to take up possibly isolating practices in order to reduce and eliminate the spread of the virus, it is important to develop or take up new forms of prosocial yet physically distant dynamics in order to address the negative psychological impact of these measures. The negative consequences of public health interventions might increase feelings loneliness and isolation experienced within Western industrialized societies. For this reason, teletherapy serves as temporary (and limited) intervention that could ameliorate the psychological effects of isolation. It could also serve as a space for the development of critical consciousness, as people reflect on the sociopolitical and economic impacts these measures have on them, and how they wish to address them. Nevertheless, we also offer an ethical cautionary tale to the application of teletherapy beyond the current emergency pandemic of the COVID-19.

scholarly journals Evolution of disease transmission rate during the course of SARS-COV-2: Patterns and determinants

2020 ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Reproduction Number ◽  
Transmission Rate ◽  
Region Of Interest ◽  
Health Interventions ◽  
Transmission Model ◽  
Public Health Interventions ◽  
Containment Measures ◽  
The Impact

Abstract To date, many studies have argued the potential impact of public health interventions on flattening the epidemic curve of SARS-CoV-2. Most of them have focused on simulating the impact of interventions in a region of interest by manipulating contact patterns and key transmission parameters to reflect different scenarios. Our study looks into the evolution of the daily effective reproduction number during the epidemic via a stochastic transmission model. We found this measure (although model-dependent) provides an early signal of the efficacy of containment measures. This epidemiological parameter when updated in real-time can also provide better predictions of future outbreaks. Our results found a substantial variation in the effect of public health interventions on the dynamic of SARS-CoV-2 transmission over time and across countries, that could not be explained solely by the timing and number of the adopted interventions. This suggests that further knowledge about the idiosyncrasy of their implementation and effectiveness is required. Although sustained containment measures have successfully lowered growth in disease transmission, more than half of the 101 studied countries failed to maintain the effective reproduction number close to or below 1. This resulted in continued growth in reported cases. Finally, we were able to predict with reasonable accuracy which countries would experience outbreaks in the next 30 days.

scholarly journals The impact of public health interventions on the future prevalence of ESBL-producing Klebsiella pneumoniae: a population based mathematical modelling study

2019 ◽  
Author(s):  
Luisa Salazar-Vizcaya ◽  
Andrew Atkinson ◽  
Andreas Kronenberg ◽  
Catherine Plüss-Suard ◽  
Roger Kouyos ◽  
...  
Keyword(s):  
Public Health ◽  
Transmission Rate ◽  
Health Interventions ◽  
Resistant Bacteria ◽  
Antimicrobial Consumption ◽  
Public Health Interventions ◽  
The Future ◽  
Non Local ◽  
The Impact ◽  
Local Transmission

AbstractBackgroundExtended-spectrum betalactamase (ESBL-) producing K. pneumoniae is one of the most common causes of infections with antimicrobial resistant bacteria worldwide. The spread of colonization of humans with this pathogen is on the rise. The future prevalence of colonization with ESBL-producing K. pneumoniae, and the potential of public health interventions to lower it, remain uncertain.MethodsBased on detailed data on antimicrobial consumption and susceptibility systematically recorded for over 13 years in a Swiss region, we developed a mathematical model to i) reconstruct the observed course of colonization with ESBL-producing K. pneumoniae; and ii) to assess the potential impact of public health interventions on future trends in colonization.ResultsSimulated prevalence of colonization with ESBL-producing K. pneumoniae stabilized in the near future when rates of antimicrobial consumption and in-hospital transmission remained stable in the main analyses (simulated prevalence in 2025 was 5.3% (5.0%-9.1%) in hospitals and 2.7% (2.1%-4.6%) in the community versus 5.6% (5.1%-9.5%) and 2.8% (2.2%-5.0%) in 2019). The largest changes in future prevalence were observed in simulations that assumed changes in overall antimicrobial consumption. When overall antimicrobial consumption was set to decrease by 50%, prevalence in 2025 declined by 89% in hospitals and by 84% in the community. A 50% decline in transmission rate within hospitals led to a reduction in prevalence of 43% in hospitals and of 13% in the community by 2025. Prevalence changed much less (≤9%) across scenarios with reduced carbapenem consumption. Assuming higher rates for the contribution from external sources of colonization, led to decreasing estimations of future prevalence in hospitals. While high uncertainty remains on the magnitude of these contribution, the best model fit suggested that as much as 46% (95% CI: 12%-96 %) of observed colonizations could be attributable to sources other than human-to-human transmission within the geographical setting (i.e., non-local transmission).ConclusionsThis study suggests that overall antimicrobial consumption will be, by far, the most powerful driver of future prevalence and that a large fraction of colonizations could be attributed to non-local transmission.

scholarly journals Intervention strategies against COVID-19 and their estimated impact on Swedish healthcare capacity

2020 ◽  
Author(s):  
Jasmine M Gardner ◽  
Lander Willem ◽  
Wouter Van Der Wijngaart ◽  
Shina Caroline Lynn Kamerlin ◽  
Nele Brusselaers ◽  
...  
Keyword(s):  
Public Health ◽  
Intensive Care ◽  
Healthcare System ◽  
Healthcare Workers ◽  
Health Interventions ◽  
Parameter Estimates ◽  
List Type ◽  
Public Health Interventions ◽  
Social Distancing ◽  
Health Measures

AbstractObjectivesDuring March 2020, the COVID-19 pandemic has rapidly spread globally, and non-pharmaceutical interventions are being used to reduce both the load on the healthcare system as well as overall mortality.DesignIndividual-based transmission modelling using Swedish demographic and Geographical Information System data and conservative COVID-19 epidemiological parameters.SettingSwedenParticipantsA model to simulate all 10.09 million Swedish residents.Interventions5 different non-pharmaceutical public-health interventions including the mitigation strategy of the Swedish government as of 10 April; isolation of the entire household of confirmed cases; closure of schools and non-essential businesses with or without strict social distancing; and strict social distancing with closure of schools and non-essential businesses.Main outcome measuresEstimated acute care and intensive care hospitalisations, COVID-19 attributable deaths, and infections among healthcare workers from 10 April until 29 June.FindingsOur model for Sweden shows that, under conservative epidemiological parameter estimates, the current Swedish public-health strategy will result in a peak intensive-care load in May that exceeds pre-pandemic capacity by over 40-fold, with a median mortality of 96,000 (95% CI 52,000 to 183,000). The most stringent public-health measures examined are predicted to reduce mortality by approximately three-fold. Intensive-care load at the peak could be reduced by over two-fold with a shorter period at peak pandemic capacity.ConclusionsOur results predict that, under conservative epidemiological parameter estimates, current measures in Sweden will result in at least 40-fold over-subscription of pre-pandemic Swedish intensive care capacity, with 15.8 percent of Swedish healthcare workers unable to work at the pandemic peak. Modifications to ICU admission criteria from international norms would further increase mortality.What is already known?-The COVID-19 pandemic has spread rapidly in Europe and globally since March 2020.-Mitigation and suppression methods have been suggested to slow down or halt the spread of the COVID-19 pandemic. Most European countries have enacted strict suppression measures including lockdown, school closures, enforced social distancing; while Sweden has chosen a different strategy of milder mitigation as of today (10 April 2020).-Different national policy decisions have been justified by socio-geographic differences among countries. Such differences as well as the tempo and stringency of public-health interventions are likely to affect the impact on each country’s mortality and healthcare system.What this study adds?-Individual-based modelling of COVID-19 spread using Swedish demographics and conservative epidemiological assumptions indicates that the peak of the number of hospitalised patients with COVID-19 can be expected in early May under the current strategy, shifted earlier and attenuated with more stringent public health measures.-Healthcare needs are expected to substantially exceed pre-pandemic capacity even if the most aggressive interventions considered were implemented in the coming weeks. In particular the need for intensive care unit beds will be at least 40-fold greater than the pre-pandemic capacity if the current strategy is maintained, and at least 10-fold greater if strategies approximating the most stringent in Europe are introduced by 10 April.-Our model predicts that, using median infection-fatality-rate estimates, at least 96,000 deaths would occur by 1 July without mitigation. Current policies reduce this number by approximately 15%, while even more aggressive social distancing measures, such as adding household isolation or mandated social distancing can reduce this number by more than 50%.

scholarly journals Evolution of disease transmission during the COVID-19 pandemic: patterns and determinants

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jie Zhu ◽  
Blanca Gallego
Keyword(s):  
Public Health ◽  
Disease Transmission ◽  
Clustering Algorithm ◽  
Health Interventions ◽  
Transmission Model ◽  
Model Parameters ◽  
Public Health Interventions ◽  
Forecasting Accuracy ◽  
Time Lagged ◽  
The Impact

AbstractEpidemic models are being used by governments to inform public health strategies to reduce the spread of SARS-CoV-2. They simulate potential scenarios by manipulating model parameters that control processes of disease transmission and recovery. However, the validity of these parameters is challenged by the uncertainty of the impact of public health interventions on disease transmission, and the forecasting accuracy of these models is rarely investigated during an outbreak. We fitted a stochastic transmission model on reported cases, recoveries and deaths associated with SARS-CoV-2 infection across 101 countries. The dynamics of disease transmission was represented in terms of the daily effective reproduction number ($$R_t$$ R t ). The relationship between public health interventions and $$R_t$$ R t was explored, firstly using a hierarchical clustering algorithm on initial $$R_t$$ R t patterns, and secondly computing the time-lagged cross correlation among the daily number of policies implemented, $$R_t$$ R t , and daily incidence counts in subsequent months. The impact of updating $$R_t$$ R t every time a prediction is made on the forecasting accuracy of the model was investigated. We identified 5 groups of countries with distinct transmission patterns during the first 6 months of the pandemic. Early adoption of social distancing measures and a shorter gap between interventions were associated with a reduction on the duration of outbreaks. The lagged correlation analysis revealed that increased policy volume was associated with lower future $$R_t$$ R t (75 days lag), while a lower $$R_t$$ R t was associated with lower future policy volume (102 days lag). Lastly, the outbreak prediction accuracy of the model using dynamically updated $$R_t$$ R t produced an average AUROC of 0.72 (0.708, 0.723) compared to 0.56 (0.555, 0.568) when $$R_t$$ R t was kept constant. Monitoring the evolution of $$R_t$$ R t during an epidemic is an important complementary piece of information to reported daily counts, recoveries and deaths, since it provides an early signal of the efficacy of containment measures. Using updated $$R_t$$ R t values produces significantly better predictions of future outbreaks. Our results found variation in the effect of early public health interventions on the evolution of $$R_t$$ R t over time and across countries, which could not be explained solely by the timing and number of the adopted interventions.

scholarly journals Harnessing testing strategies and public health measures to avert COVID-19 outbreaks during ocean cruises

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Gerardo Chowell ◽  
Sushma Dahal ◽  
Raquel Bono ◽  
Kenji Mizumoto
Keyword(s):  
Public Health ◽  
Cost Effective ◽  
Contact Network ◽  
Social Distancing ◽  
Individual Level ◽  
Health Measures ◽  
Testing Strategies ◽  
Novel Coronavirus ◽  
The Individual ◽  
The Impact

AbstractTo ensure the safe operation of schools, workplaces, nursing homes, and other businesses during COVID-19 pandemic there is an urgent need to develop cost-effective public health strategies. Here we focus on the cruise industry which was hit early by the COVID-19 pandemic, with more than 40 cruise ships reporting COVID-19 infections. We apply mathematical modeling to assess the impact of testing strategies together with social distancing protocols on the spread of the novel coronavirus during ocean cruises using an individual-level stochastic model of the transmission dynamics of COVID-19. We model the contact network, the potential importation of cases arising during shore excursions, the temporal course of infectivity at the individual level, the effects of social distancing strategies, different testing scenarios characterized by the test’s sensitivity profile, and testing frequency. Our findings indicate that PCR testing at embarkation and daily testing of all individuals aboard, together with increased social distancing and other public health measures, should allow for rapid detection and isolation of COVID-19 infections and dramatically reducing the probability of onboard COVID-19 community spread. In contrast, relying only on PCR testing at embarkation would not be sufficient to avert outbreaks, even when implementing substantial levels of social distancing measures.

The Impact of Public Health Interventions on Delaying and Mitigating against Replacement by SARS-CoV-2 Variants of Concern

2021 ◽  
Author(s):  
Jianhong Wu ◽  
Francesca Scarabel ◽  
Bushra Majeed ◽  
Nicola Luigi Bragazzi ◽  
James Orbinski
Keyword(s):  
Public Health ◽  
Health Interventions ◽  
Public Health Interventions ◽  
The Impact

Syphilis epidemiology and public health interventions in Western Australia from 1991 to 2009

Sexual Health ◽  
2012 ◽  
Vol 9 (3) ◽  
pp. 272 ◽  
Author(s):  
Kellie S. H. Kwan ◽  
Carolien M. Giele ◽  
Heath S. Greville ◽  
Carole A. Reeve ◽  
P. Heather Lyttle ◽  
...  
Keyword(s):  
Public Health ◽  
Western Australia ◽  
Aboriginal People ◽  
Congenital Syphilis ◽  
The United States ◽  
Health Interventions ◽  
Public Health Interventions ◽  
Sex With Men ◽  
Notification Data ◽  
The Impact

Objectives To describe the epidemiology of congenital and infectious syphilis during 1991–2009, examine the impact of public health interventions and discuss the feasibility of syphilis elimination among Aboriginal people in Western Australia (WA). Methods: WA congenital and infectious syphilis notification data in 1991–2009 and national infectious syphilis notification data in 2005–2009 were analysed by Aboriginality, region of residence, and demographic and behavioural characteristics. Syphilis public health interventions in WA from 1991–2009 were also reviewed. Results: During 1991–2009, there were six notifications of congenital syphilis (50% Aboriginal) and 1441 infectious syphilis notifications (61% Aboriginal). During 1991–2005, 88% of notifications were Aboriginal, with several outbreaks identified in remote WA. During 2006–2009, 62% of notifications were non-Aboriginal, with an outbreak in metropolitan men who have sex with men. The Aboriginal : non-Aboriginal rate ratio decreased from 173 : 1 (1991–2005) to 15 : 1 (2006–2009). Conclusions: These data demonstrate that although the epidemiology of syphilis in WA has changed over time, the infection has remained endemic among Aboriginal people in non-metropolitan areas. Given the continued public health interventions targeted at this population, the limited success in eliminating syphilis in the United States and the unique geographical and socioeconomic features of WA, the elimination of syphilis seems unlikely in this state.

The Economics of Infectious Diseases

2018 ◽  
Author(s):  
Katharina Hauck
Keyword(s):  
Public Health ◽  
Infectious Diseases ◽  
Disease Transmission ◽  
Health Interventions ◽  
Health System Strengthening ◽  
Economic Research ◽  
Economic Evaluations ◽  
Research Needs ◽  
Public Health Interventions ◽  
The Impact

Economics can make immensely valuable contributions to our understanding of infectious disease transmission and the design of effective policy responses. The one unique characteristic of infectious diseases makes it also particularly complicated to analyze: the fact that it is transmitted from person to person. It explains why individuals’ behavior and externalities are a central topic for the economics of infectious diseases. Many public health interventions are built on the assumption that individuals are altruistic and consider the benefits and costs of their actions to others. This would imply that even infected individuals demand prevention, which stands in conflict with the economic theory of rational behavior. Empirical evidence is conflicting for infected individuals. For healthy individuals, evidence suggests that the demand for prevention is affected by real or perceived risk of infection. However, studies are plagued by underreporting of preventive behavior and non-random selection into testing. Some empirical studies have shown that the impact of prevention interventions could be far greater than one case prevented, resulting in significant externalities. Therefore, economic evaluations need to build on dynamic transmission models in order to correctly estimate these externalities. Future research needs are significant. Economic research needs to improve our understanding of the role of human behavior in disease transmission; support the better integration of economic and epidemiological modeling, evaluation of large-scale public health interventions with quasi-experimental methods, design of optimal subsidies for tackling the global threat of antimicrobial resistance, refocusing the research agenda toward underresearched diseases; and most importantly to assure that progress translates into saved lives on the ground by advising on effective health system strengthening.

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