Characterizing the Impact of Social Inequality on COVID-19 Propagation in Developing Countries

The world faces a pandemic not previously experienced in modern times. The internal mechanism of SARS-Cov-2 is not well known and there are no Pharmaceutical Interventions available. To stem the spread of the virus, measures of respiratory etiquette, social distancing and hand hygiene have been recommended. Based on these measures, some countries have already managed to control the COVID-19 propagation, although in the absence of pharmaceutical interventions, this control is not definitive. However, we have seen that social heterogeneity across populations makes the effects of COVID-19 also different. Social inequality affects the population of developing countries not only from an economic point of view. The relationship between social inequality and the health condition is not new, but it becomes even more evident in times of crisis, such as the one the world has been facing with COVID-19. How does social inequality affect the COVID-19 propagation in developing countries is the object of this study. We propose a new epidemic SEIR model based on social indicators to predict outbreak and mortality of COVID-19. The estimated number of infected and fatalities are compared with different levels of Non-Pharmaceutical Interventions. We present a case study for the Deep Brazil. The results showed that social inequality has a strong effect on the propagation of COVID-19, increasing its damage and accelerating the collapse of health infrastructure.

[1]  Aaron van Dorn,et al.  COVID-19 exacerbating inequalities in the US , 2020, The Lancet.

[2]  W. Ramalho,et al.  Expected impact of COVID-19 outbreak in a major metropolitan area in Brazil , 2020, medRxiv.

[3]  PIOTR STASZKIEWICZ,et al.  Dynamics of the COVID-19 Contagion and Mortality: Country Factors, Social Media, and Market Response Evidence From a Global Panel Analysis , 2020, IEEE Access.

[4]  Jason Beckfield,et al.  An institutional theory of welfare state effects on the distribution of population health , 2015 .

[5]  Pedro Pequeno,et al.  Air transportation, population density and temperature predict the spread of COVID-19 in Brazil , 2020, PeerJ.

[6]  R. Barata Como e por que as desigualdades sociais fazem mal à saúde , 2009 .

[7]  R. Adhikari,et al.  Age-structured impact of social distancing on the COVID-19 epidemic in India , 2020, 2003.12055.

[8]  K. Yuen,et al.  Clinical Characteristics of Coronavirus Disease 2019 in China , 2020, The New England journal of medicine.

[9]  Rui Ji,et al.  Prevalence of comorbidities and its effects in patients infected with SARS-CoV-2: a systematic review and meta-analysis , 2020, International Journal of Infectious Diseases.

[10]  World Health Organization,et al.  Calibrating Long-Term Non-Pharmaceutical Interventions for COVID-19: Principles and Facilitation Tools , 2020 .

[11]  Xianbin Li,et al.  Prediction of New Coronavirus Infection Based on a Modified SEIR Model , 2020, medRxiv.

[12]  Juan-Carlos Cano,et al.  Evaluating How Smartphone Contact Tracing Technology Can Reduce the Spread of Infectious Diseases: The Case of COVID-19 , 2020, IEEE Access.

[13]  S. Bhatt,et al.  Report 12: The global impact of COVID-19 and strategies for mitigation and suppression , 2020 .

[14]  C. Faes,et al.  Estimating the generation interval for coronavirus disease (COVID-19) based on symptom onset data, March 2020 , 2020, Euro surveillance : bulletin Europeen sur les maladies transmissibles = European communicable disease bulletin.

[15]  Nuno R. Faria,et al.  Report 21: Estimating COVID-19 cases and reproduction number in Brazil , 2020, medRxiv.

[16]  Chunming Qiao,et al.  PocketCare: Tracking the Flu with Mobile Phones using Partial Observations of Proximity and Symptoms , 2019, Proc. ACM Interact. Mob. Wearable Ubiquitous Technol..

[17]  Marco De Nadai,et al.  Mobile phone data for informing public health actions across the COVID-19 pandemic life cycle , 2020, Science Advances.

[18]  P. Klepac,et al.  Feasibility of controlling COVID-19 outbreaks by isolation of cases and contacts , 2020, The Lancet Global Health.

[19]  C. Whittaker,et al.  Report 9: Impact of non-pharmaceutical interventions (NPIs) to reduce COVID19 mortality and healthcare demand , 2020 .

[20]  Jing Li,et al.  Early Prediction of the 2019 Novel Coronavirus Outbreak in the Mainland China Based on Simple Mathematical Model , 2020, IEEE Access.

[21]  M. Kretzschmar,et al.  Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods , 2014, BMC Public Health.

[22]  G. Ohlin The Organization for Economic Cooperation and Development , 1968, International Organization.

[23]  X. Wang,et al.  Controlling the Hidden Growth of COVID-19 , 2020, 2005.09769.

[24]  Clare Bambra,et al.  The socioeconomic distribution of non‐communicable diseases in Europe: findings from the European Social Survey (2014) special module on the social determinants of health , 2017, European journal of public health.

[25]  W. Liang,et al.  Modified SEIR and AI prediction of the epidemics trend of COVID-19 in China under public health interventions , 2020, Journal of thoracic disease.

[26]  D. Wang,et al.  The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak – an update on the status , 2020, Military Medical Research.

[27]  C. Anastassopoulou,et al.  Data-based analysis, modelling and forecasting of the COVID-19 outbreak , 2020, medRxiv.

[28]  M. R. Ferrández,et al.  Mathematical modeling of the spread of the coronavirus disease 2019 (COVID-19) taking into account the undetected infections. The case of China , 2020, Communications in Nonlinear Science and Numerical Simulation.

[29]  C. Willmott,et al.  Advantages of the mean absolute error (MAE) over the root mean square error (RMSE) in assessing average model performance , 2005 .

[30]  Zhilan Feng,et al.  Influence of non-homogeneous mixing on final epidemic size in a meta-population model , 2018, Journal of biological dynamics.

[31]  P. Colaneri,et al.  Modelling the COVID-19 epidemic and implementation of population-wide interventions in Italy , 2020, Nature Medicine.

[32]  Gyu Sang Choi,et al.  COVID-19 Future Forecasting Using Supervised Machine Learning Models , 2020, IEEE Access.

[33]  D. Silva,et al.  Pesos longitudinais para a Pesquisa Nacional por Amostra de Domicílios contínua (PNAD contínua) , 2019 .