scholarly journals A data-driven model of the COVID-19 spread among interconnected populations: epidemiological and mobility aspects following the lockdown in Italy

2021 ◽  
Author(s):  
Paolo Di Giamberardino ◽  
Daniela Iacoviello ◽  
Federico Papa ◽  
Carmela Sinisgalli
Keyword(s):  
Group Structure ◽  
Human Mobility ◽  
Epidemiological Data ◽  
Group Model ◽  
Disease Evolution ◽  
Analytical Stability ◽  
Containment Measures ◽  
Disease Diffusion ◽  
The Impact ◽  
Second Wave

AbstractAn epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the inter-regional mobility restored at the end of the first strong lockdown in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March–October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of the production, trade, and education activities, determined a time advance of the contagion increase since September 2020.

scholarly journals A Data-driven Model of the COVID-19 Spread among Interconnected Populations: Epidemiological and Mobility Aspects Following the Lock-down in Italy

2021 ◽  
Author(s):  
Paolo DI GIAMBERARDINO ◽  
Daniela Iacoviello ◽  
Federico Papa ◽  
Carmela Sinisgalli
Keyword(s):  
Group Structure ◽  
Human Mobility ◽  
Epidemiological Data ◽  
Group Model ◽  
Disease Evolution ◽  
Analytical Stability ◽  
Containment Measures ◽  
Disease Diffusion ◽  
The Impact ◽  
Second Wave

Abstract An epidemic multi-group model formed by interconnected SEIR-like structures is formulated and used for data fitting to gain insight into the COVID-19 dynamics and into the role of non-pharmaceutical control actions implemented to limit the infection spread since its outbreak in Italy. The single submodels provide a rather accurate description of the COVID-19 evolution in each subpopulation by an extended SEIR model including the class of asymptomatic infectives, which is recognized as a determinant for disease diffusion. The multi-group structure is specifically designed to investigate the effects of the interregional mobility restored at the end of the first strong lock-down in Italy (June 3, 2020). In its time-invariant version, the model is shown to enjoy some analytical stability properties which provide significant insights on the efficacy of the implemented control measurements. In order to highlight the impact of human mobility on the disease evolution in Italy between the first and second wave onset, the model is applied to fit real epidemiological data of three geographical macro-areas in the period March-October 2020, including the mass departure for summer holidays. The simulation results are in good agreement with the data, so that the model can represent a useful tool for predicting the effects of the combination of containment measures in triggering future pandemic scenarios. Particularly, the simulation shows that, although the unrestricted mobility alone appears to be insufficient to trigger the second wave, the human transfers were crucial to make uniform the spatial distribution of the infection throughout the country and, combined with the restart of (production, trade and education) activities, determined a time advance of the contagion increase (autumn 2020).

scholarly journals Impact of data accuracy on the evaluation of COVID-19 mitigation policies

Data & Policy ◽  
2021 ◽  
Vol 3 ◽  
Author(s):  
Michele Starnini ◽  
Alberto Aleta ◽  
Michele Tizzoni ◽  
Yamir Moreno
Keyword(s):  
Epidemiological Data ◽  
Surveillance Data ◽  
Inaccurate Data ◽  
Health Authorities ◽  
The Social ◽  
Containment Measures ◽  
Public Health Authorities ◽  
Nonpharmaceutical Interventions ◽  
The Impact ◽  
Extreme Care

Abstract Evaluating the effectiveness of nonpharmaceutical interventions (NPIs) to mitigate the COVID-19 pandemic is crucial to maximize the epidemic containment while minimizing the social and economic impact of these measures. However, this endeavor crucially relies on surveillance data publicly released by health authorities that can hide several limitations. In this article, we quantify the impact of inaccurate data on the estimation of the time-varying reproduction number $ R(t) $ , a pivotal quantity to gauge the variation of the transmissibility originated by the implementation of different NPIs. We focus on Italy and Spain, two European countries among the most severely hit by the COVID-19 pandemic. For these two countries, we highlight several biases of case-based surveillance data and temporal and spatial limitations in the data regarding the implementation of NPIs. We also demonstrate that a nonbiased estimation of $ R(t) $ could have had direct consequences on the decisions taken by the Spanish and Italian governments during the first wave of the pandemic. Our study shows that extreme care should be taken when evaluating intervention policies through publicly available epidemiological data and call for an improvement in the process of COVID-19 data collection, management, storage, and release. Better data policies will allow a more precise evaluation of the effects of containment measures, empowering public health authorities to take more informed decisions.

scholarly journals THE IMPACT OF LOCKDOWN ON PUBLIC HEALTH DURING THE FIRST WAVE OF COVID-19 PANDEMIC: LESSONS LEARNED FOR DESIGNING EFFECTIVE CONTAINMENT MEASURES TO COPE WITH SECOND WAVE

2020 ◽  
Author(s):  
Mario Coccia
Keyword(s):  
Public Health ◽  
Social Issues ◽  
Negative Impact ◽  
Lessons Learned ◽  
Fatality Rate ◽  
Negative Effects ◽  
Global Pandemic ◽  
Containment Measures ◽  
The Impact ◽  
Second Wave

AbstractWhat is hardly known in the studies of the COVID-19 global pandemic crisis is the impact of general lockdown during the first wave of COVID-19 pandemic both public health and economic system. The main goal of this study is a comparative analysis of some European countries with a longer and shorter period of national lockdown during the first wave of COVID-19 from March to August 2020. Findings suggests that: a) countries with shorter period of lockdown have a variation of confirmed cases/population (%) higher than countries with longer period of lockdown; b) countries with shorter period of lockdown have average fatality rate (5.45%) lower than countries with longer period of lockdown (12.70%), whereas variation of fatality rate from August to March 2020 suggests a higher reduction in countries with longer period of lockdown (−1.9% vs 0.72%). However, Independent Samples Test and the Mann-Whitney test reveal that the effectiveness of longer period of lockdown versus shorter one on public health is not significant. In addition, the COVID-19 pandemic associated with longer period of lockdown has a higher negative impact on economic growth with consequential social issues in countries. Results of the impact of COVID-19 lockdowns on public health and economies of some leading countries in Europe, during the first wave of the COVID-19 pandemic, can provide vital information to design effective containment strategies in future waves of this pandemic to minimize the negative effects in society.

scholarly journals Age-stratified model of the COVID-19 epidemic to analyze the impact of relaxing lockdown measures: nowcasting and forecasting for Switzerland

2020 ◽  
Author(s):  
Fadoua Balabdaoui ◽  
Dirk Mohr
Keyword(s):  
Infected Individual ◽  
Compartment Model ◽  
Policy Makers ◽  
Modern Health Care ◽  
Containment Measures ◽  
To Come ◽  
The Impact ◽  
Second Wave ◽  
Important Basis ◽  
Stratified Model

AbstractCompartmental models enable the analysis and prediction of an epidemic including the number of infected, hospitalized and deceased individuals in a population. They allow for computational case studies on non-pharmaceutical interventions thereby providing an important basis for policy makers. While research is ongoing on the transmission dynamics of the SARS-CoV-2 coronavirus, it is important to come up with epidemic models that can describe the main stages of the progression of the associated COVID-19 respiratory disease. We propose an age-stratified discrete compartment model as an alternative to differential equation based S-I-R type of models. The model captures the highly age-dependent progression of COVID-19 and is able to describe the day-by-day advancement of an infected individual in a modern health care system. The fully-identified model for Switzerland not only predicts the overall histories of the number of infected, hospitalized and deceased, but also the corresponding age-distributions. The model-based analysis of the outbreak reveals an average infection fatality ratio of 0.4% with a pronounced maximum of 9.5% for those aged ≥80 years. The predictions for different scenarios of relaxing the soft lockdown indicate a low risk of overloading the hospitals through a second wave of infections. However, there is a hidden risk of a significant increase in the total fatalities (by up to 200%) in case schools reopen with insufficient containment measures in place.

scholarly journals Evaluation of Work Resumption Strategies after COVID-19 Reopening in the Chinese City of Shenzhen: A Mathematical Modeling Study

2020 ◽  
Author(s):  
Lu Bai ◽  
Haonan Lu ◽  
Hailin Hu ◽  
M. Kumi Smith ◽  
Katherine Harripersaud ◽  
...  
Keyword(s):  
Population Control ◽  
Epidemiological Data ◽  
Population Mobility ◽  
Mobility Data ◽  
Intervention Measures ◽  
Imported Cases ◽  
High Level ◽  
The Impact ◽  
Second Wave ◽  
Work Resumption

Abstract BackgroundAs China is facing a potential second wave of the epidemic, we reviewed and evaluated the intervention measures implemented in a major metropolitan city, Shenzhen, during the early phase of Wuhan lockdown. MethodsBased on published epidemiological data on COVID-19 and population mobility data from Baidu Qianxi, we constructed a compartmental model to evaluate the impact of work and traffic resumption on the epidemic in Shenzhen in various scenarios.ResultsImported cases account for the majority (58.6%) of the early reported cases in Shenzhen. We demonstrated that with strict inflow population control and a high level of mask usage following work resumption, various resumption schemes resulted in only an insignificant difference in the number of cumulative infections. Shenzhen may experience this second wave of infections approximately two weeks after the traffic resumption if the incidence risk in Hubei is high at the moment of resumption.ConclusionControl of imported cases and extensive use of facial masks were the key for the prevention of the COVID-19 epidemic in Shenzhen during its reopening and work resumption.

scholarly journals Modeling of COVID-19 Pandemic vis-à-vis Some Socio-Economic Factors

2022 ◽  
Vol 7 ◽  
Author(s):  
Kayode Oshinubi ◽  
Mustapha Rachdi ◽  
Jacques Demongeot
Keyword(s):  
Economic Status ◽  
Epidemiological Data ◽  
Economic Determinants ◽  
Socio Economic Variables ◽  
Social Divide ◽  
Spread Of Infection ◽  
The Impact ◽  
The Relationship ◽  
Second Wave ◽  
Socio Economic Factors

The impact of the COVID-19 epidemic on the socio-economic status of countries around the world should not be underestimated, when we consider the role it has played in various countries. Many people were unemployed, many households were careful about their spending, and a greater social divide in the population emerged in 14 different countries from the Organization for Economic Co-operation and Development (OECD) and from Africa (that is, in developed and developing countries) for which we have considered the epidemiological data on the spread of infection during the first and second waves, as well as their socio-economic data. We established a mathematical relationship between Theil and Gini indices, then we investigated the relationship between epidemiological data and socio-economic determinants, using several machine learning and deep learning methods. High correlations were observed between some of the socio-economic and epidemiological parameters and we predicted three of the socio-economic variables in order to validate our results. These results show a clear difference between the first and the second wave of the pandemic, confirming the impact of the real dynamics of the epidemic’s spread in several countries and the means by which it was mitigated.

scholarly journals The socio-spatial determinants of COVID-19 diffusion: the impact of globalisation, settlement characteristics and population

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Thomas Sigler ◽  
Sirat Mahmuda ◽  
Anthony Kimpton ◽  
Julia Loginova ◽  
Pia Wohland ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Total Population ◽  
Human Mobility ◽  
Household Size ◽  
Human Interaction ◽  
Population Characteristics ◽  
Disease Diffusion ◽  
Older Populations ◽  
The Impact ◽  
Over Time

Abstract Background COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of COVID-19 transmission, there remains an important gap in knowledge regarding the drivers of geographical diffusion between places, in particular at the global scale. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of reported COVID-19 diffusion over a six-week period in March and April 2020. Our exploratory analysis is based on reported COVID-19 data published by Johns Hopkins University which, despite its limitations, serves as the best repository of reported COVID-19 cases across nations. Results The quantile regression model suggests that globalisation, settlement, and population characteristics related to high human mobility and interaction predict reported disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total reported cases (per million) whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total reported cases (per million). Population density, and population characteristics such as total population, older populations, and household size are strong predictors in early weeks but have a muted impact over time on reported COVID-19 diffusion. In contrast, the impacts of interpersonal and trade globalisation are enhanced over time, indicating that human mobility may best explain sustained disease diffusion. Conclusions Model results confirm that globalisation, settlement and population characteristics, and variables tied to high human mobility lead to greater reported disease diffusion. These outcomes serve to inform suppression strategies, particularly as they are related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. It is likely that many of these processes are replicated at smaller geographical scales both within countries and within regions. Epidemiological strategies must therefore be tailored according to human mobility patterns, as well as countries’ settlement and population characteristics. We suggest that limiting human mobility to the greatest extent practical will best restrain COVID-19 diffusion, which in the absence of widespread vaccination may be one of the best lines of epidemiological defense.

scholarly journals The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

2020 ◽  
Author(s):  
Thomas Sigler ◽  
Sirat Mahmuda ◽  
Anthony Kimpton ◽  
Julia Loginova ◽  
Pia Wohland-Jakhar ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Human Mobility ◽  
Developed Countries ◽  
Human Interaction ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Global Pandemic ◽  
Disease Diffusion ◽  
Older Populations ◽  
The Impact

Abstract Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020.Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

scholarly journals HOW IS THE IMPACT ON PUBLIC HEALTH OF SECOND WAVE OF COVID-19 PANDEMIC COMPARED TO THE FIRST WAVE? CASE STUDY OF ITALY

2020 ◽  
Author(s):  
Mario Coccia
Keyword(s):  
Public Health ◽  
Organizational Capacity ◽  
New Wave ◽  
Policy Responses ◽  
Daily Data ◽  
Containment Measures ◽  
Effective Policy ◽  
The Impact ◽  
Second Wave

AbstractThe main goal of this study is to compare the effects on public health of the second wave of the COVID-19 pandemic compared to first wave in society. The paper here focuses on a case study of Italy, one of the first European countries to experience a rapid increase in confirmed cases and deaths. Methodology considers daily data from February to November 2020 of the ratio of confirmed cases/total swabs, fatality rate (deaths / confirmed cases) and ratio of individuals in Intensive Care Units (ICUs) / Confirmed cases. Results reveal that the first wave of COVID-19 pandemic in Italy had a strong but declining impact on public health with the approaching of summer season and with the effects of containment measures, whereas second wave of the COVID-19 has a growing trend of confirmed cases with admission to ICUs and total deaths having a, to date, lower impact on public health compared to first wave. Although effects of the first wave of the COVID-19 pandemic on public health, policymakers have had an unrealistic optimist behavior that a new wave of COVID-19 could not hit their countries and, especially, a low organizational capacity to plan effective policy responses to cope with recurring COVID-19 pandemic crisis. This study can support vital information to design effective policy responses of crisis management to constrain current and future waves of the COVID-19 pandemic and similar epidemics in society.

scholarly journals The Socio-Spatial Determinants of COVID-19 Diffusion: The Impact of Globalisation, Settlement Characteristics and Population

2020 ◽  
Author(s):  
Thomas Sigler ◽  
Sirat Mahmuda ◽  
Anthony Kimpton ◽  
Julia Loginova ◽  
Pia Wohland-Jakhar ◽  
...  
Keyword(s):  
Quantile Regression ◽  
Human Mobility ◽  
Developed Countries ◽  
Human Interaction ◽  
Population Characteristics ◽  
Mobility Patterns ◽  
Global Pandemic ◽  
Disease Diffusion ◽  
Older Populations ◽  
The Impact

Abstract Background: COVID-19 is an emergent infectious disease that has spread geographically to become a global pandemic. While much research focuses on the epidemiological and virological aspects of the COVID-19 transmission, there remains a gap in knowledge regarding the drivers of geographical diffusion between places. Here, we use quantile regression to model the roles of globalisation, human settlement and population characteristics as socio-spatial determinants of COVID-19 diffusion over a six-week period in March and April 2020. Results: The quantile regression model suggest that globalisation and settlement population characteristics related to high human mobility predict disease diffusion. Human development level (HDI) and total population predict COVID-19 diffusion in countries with a high number of total confirmed cases per million whereas larger household size, older populations, and globalisation tied to human interaction predict COVID-19 diffusion in countries with a low number of total confirmed cases per million. Conclusions: The analysis confirms that globalisation, settlement and population characteristics lead to greater disease diffusion, and primarily variables tied to high human mobility. These outcomes serve to inform policies around ‘flattening the curve’, particularly as they related to anticipated relocation diffusion from more- to less-developed countries and regions, and hierarchical diffusion from countries with higher population and density. Epidemiological strategies must be tailored to suit the range of human mobility patterns, as well as the variety of settlement and population characteristics.

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