Can economic indicators predict infectious disease spread? A cross-country panel analysis of 13 European countries

Trends and prediction in the incidence rate of hepatitis C in Shandong Province in China from 2004 to 2030

BACKGROUND

Hepatitis C threatens human health and brings a heavy economic burden. Shandong Province is the second most populous province in China and has uneven regional economic development. Therefore, we analyzed the incidence rate trend and regional differences of hepatitis C in Shandong Province from 2004 to 2021.

METHODS

The monthly and annual incidence rates of hepatitis C in Shandong Province from 2022 to 2030 were predicted by fitting Autoregressive Integrated Moving Average model (ARIMA), Long Short-Term Memory (LSTM) and ARIMA-LSTM combined model.

RESULTS

From 2004 to 2021, annual new cases of hepatitis C in Shandong Province increased from 635 to 5834, with a total of 61,707 cases. The incidence rate increased from 0.69/100 thousand in 2004 to 6.40/100 thousand in 2019, with a slight decrease in 2020 and 2021. The average annual incidence rate was 3.47/100 thousand. In terms of regional distribution, the hepatitis C incidence rate in Shandong Province was generally high in the west and low in the east. It is estimated that the hepatitis C incidence rate in Shandong Province will be 9.21 per 100 thousand in 2030.

CONCLUSION

The hepatitis C incidence rate in Shandong Province showed an increasing trend from 2004 to 2019 and a decreasing trend in 2020 and 2021. Significant regional variations in incidence rate existed. An upward trend in incidence rate is predicted from 2022 to 2030. It is necessary to strengthen the prevention and control of hepatitis C to achieve the goal of eliminating viral hepatitis by 2030.

Monkeypox outbreaks in the context of the COVID-19 pandemic: Network and clustering analyses of global risks and modified SEIR prediction of epidemic trends

Background

Ninety-eight percent of documented cases of the zoonotic disease human monkeypox (MPX) were reported after 2001, with especially dramatic global spread in 2022. This longitudinal study aimed to assess spatiotemporal risk factors of MPX infection and predict global epidemiological trends.

Method

Twenty-one potential risk factors were evaluated by correlation-based network analysis and multivariate regression. Country-level risk was assessed using a modified Susceptible-Exposed-Infectious-Removed (SEIR) model and a risk-factor-driven k-means clustering analysis.

Results

Between historical cases and the 2022 outbreak, MPX infection risk factors changed from relatively simple [human immunodeficiency virus (HIV) infection and population density] to multiple [human mobility, population of men who have sex with men, coronavirus disease 2019 (COVID-19) infection, and socioeconomic factors], with human mobility in the context of COVID-19 being especially key. The 141 included countries classified into three risk clusters: 24 high-risk countries mainly in West Europe and Northern America, 70 medium-risk countries mainly in Latin America and Asia, and 47 low-risk countries mainly in Africa and South Asia. The modified SEIR model predicted declining transmission rates, with basic reproduction numbers ranging 1.61-7.84 in the early stage and 0.70-4.13 in the current stage. The estimated cumulative cases in Northern and Latin America may overtake the number in Europe in autumn 2022.

Conclusions

In the current outbreak, risk factors for MPX infection have changed and expanded. Forecasts of epidemiological trends from our modified SEIR models suggest that Northern America and Latin America are at greater risk of MPX infection in the future.

Impact of social and cultural factors on incidence, transmission and control of Coronavirus disease in Iran: a qualitative study

INTRODUCTION

COVID-19 pandemic has had mixed reactions from nations, people and governments about ways to cope with, prevent and control the disease. The current study identifies social, cultural and policy factors affecting the incidence and control of Coronavirus disease in Iran.

METHODS

A qualitative study consists of content analysis as well as the views of 20 experienced and knowledgeable subjects specialized in social and cultural health management. The data were gathered using three semi-structured interviews and then continued by 17 semi-structured interviews. Data analysis was done using Graneheim approach. After each interview, the recorded audio files transcript and reviewed. Then codes extracted and divided to categories and sub-categories.

RESULTS

There are distinct social and cultural factors in coping with Coronavirus disease. These consisted of three categories of governance, individual and community related factors. A total of 17 subcategories and 215 primary codes that were extracted from the text of interviews as variables of the study and in relation to the research question. Ten subdomains of governance including vaccination, political issues, knowledge, support services, administrative services, transportation, health and treatment, culturalization, legislation and, managerial and financial policies impacted the spread and mitigation of the pandemic at various levels.

CONCLUSION

The management of pandemics requires a comprehensive capacity for identifying and determining social and cultural criteria. A healthy partnership between governments and the community may be required to remove unnecessary obstacles that hinder public health attempt to alleviate the risk. The obtained criteria and indicators from this study may be utilized by policy makers in an attempt to strengthen protocols for mitigating pandemics. Further studies may be warranted to confirm these findings.

The sources of the Kuznets relationship between the COVID-19 mortality rate and economic performance

This paper discusses the findings of an empirical analysis of the Kuznets, or reverse U-shaped relationship, between the COVID-19 mortality rate and economic performance. In the early stages of economic development, the COVID-19 mortality rate is anticipated to rise with rising economic activity and urbanization. Eventually, the mortality rate decreases at higher economic development levels as people and the government are more capable of investing in disease abatement measures. The quality of political institutions, wealth distribution, urbanization, vaccination rate, and improvements in healthcare systems are hypothesized to affect the COVID-19 mortality rate. Examining this relationship can be effective in understanding the change in the COVID-19 mortality rate at different economic performance stages and in identifying appropriate preventive measures. This study employed the negative binomial regression to model a cross-sectional dataset of 137 countries. Results indicated that the relationship between the per-head gross domestic product (GDP) level and the COVID-19 mortality rate appeared to follow a pattern like the Kuznets curve, implying that changes in institutional quality, healthcare advancements, wealth distribution, urbanization, vaccination rate, and the percentage of the elderly population were significant in explaining the relationship. Improvement of the healthcare system has a notable effect on lowering the COVID-19 mortality rate under more effective government conditions. Additionally, the results suggested that a higher per-head GDP is required to reverse the rising trend of the mortality rate under higher income inequality. Based on these results, preventive measures, and policies to reduce COVID-19 mortalities were recommended in the conclusion section.

Measles resurgence in Brazil: analysis of the 2019 epidemic in the state of São Paulo

OBJECTIVE

To analyze the epidemiological profile of cases and the pattern of spatial diffusion of the largest measles epidemic in Brazil that occurred in the post-elimination period in the state of São Paulo.

METHOD

A cross-sectional study based on confirmed measles cases in 2019. Bivariate analysis was performed for socioeconomic, clinical, and epidemiological variables, according to prior vaccination and hospitalization, combined with an analysis of spatial diffusion of cases using the Inverse Distance Weighting (IDW) method.

RESULTS

Of the 15,598 confirmed cases, 2,039 were hospitalized and 17 progressed to death. The epidemic peak occurred in epidemiological week 33, after confirmation of the first case, in the epidemiological week 6. Most cases were male (52.1%), aged between 18 and 29 years (38.7%), identified as whites (70%). Young adults (39.7%) and children under five years (32.8%) were the most affected age groups. A higher proportion of previous vaccination was observed in whites as compared to Blacks, browns, yellows and indigenous people (p < 0.001), as well as in the most educated group compared to the other categories (p < 0.001). The risk of hospitalization was higher in children than in the older age group (RI = 2.19; 95%CI: 1.66-2.88), as well as in the unvaccinated than in the vaccinated (RI = 1.59; 95%CI: 1.45-1.75). The pattern of diffusion by contiguity combined with diffusion by relocation followed the urban hierarchy of the main cities' regions of influence.

CONCLUSION

In addition to routine vaccination in children, the findings indicate the need for immunization campaigns for young adults. In addition, studies that seek to investigate the occurrence of clusters of vulnerable populations, prone to lower vaccination coverage, are essential to broaden the understanding of the dynamics of transmission and, thus, reorienting control strategies that ensure disease elimination.

Business Cycle and Public Health: The Moderating Role of Health Education and Digital Economy

The cyclicality of public health in the emerging market is underexplored in existing literature. In this study, we used a fixed effect model and provincial data to document how public health varies with the business cycle in China over the period of 2010-2019. The estimated results showed that the business cycle is negatively correlated with the mortality of infectious disease, a proxy variable of public health, thus indicating that public health exhibits a countercyclical pattern in China. Furthermore, we investigated the potential moderating role of public health education and digital economy development in the relationship between business cycle and public health. Our findings suggested that public health education and digital economy development can mitigate the damage of economic conditions on public health in China. Health education helps the public obtain more professional knowledge about diseases and then induces effective preventions. Compared with traditional economic growth, digital economy development can avoid environmental pollution which affects public health. Also, it ensures that state-of-the-art medical services are available for the public through e-health. In addition, digitalization assures that remote working is practicable and reduces close contact during epidemics such as COVID-19. The conclusions stand when subjected to several endogeneity and robustness checks. Therefore, the paper implies that these improvements in public health education and digitalization can help the government in promoting public health.

Identificação de áreas de risco e fatores associados à epidemia de sarampo de 2019 no Estado de São Paulo, Brasil

O objetivo foi analisar a ocorrência de clusters e fatores associados ao ressurgimento de casos de sarampo da maior epidemia do período pós-eliminação, ocorrida no Estado de São Paulo, Brasil, em 2019. Fatores sociossanitários e assistenciais foram analisados por modelos de Poisson inflacionado de zero (ZIP) e ZIP com efeito espacial estruturado e não estruturado. A estatística de varredura SCAN foi usada para analisar a ocorrência de clusters de casos. Foram identificados clusters de casos de alto risco em municípios que compõem a região intermediária de São Paulo. No modelo ZIP, foram observadas como fatores de risco no nível municipal as variáveis chefes de domicílio menores de 18 anos (RR ajustado = 1,39; ICr95%: 1,27-1,53), desigualdade na distribuição de renda (RR ajustado = 36,67; ICr95%: 26,36-51,15), desocupação em maiores de 18 anos (RR ajustado = 1,10; ICr95%: 1,08-1,12) e iluminação pública inexistente (RR ajustado = 1,05; ICr95%: 1,04-1,05). Nos modelos ZIP com efeito espacial estruturado e não estruturado, foram identificados como fatores de risco os indicadores chefes de domicílio menores de 18 anos (RR ajustado = 1,36; ICr95%: 1,04-1,90) e desigualdade na distribuição dos rendimentos do trabalho (RR ajustado = 3,12; ICr95%: 1,02-9,48). Em ambos os modelos, a cobertura de agentes de saúde se apresentou como fator de proteção. Os achados reforçam a importância de intensificar as ações de vigilância de sarampo articuladas à Estratégia Saúde da Família, especialmente em áreas de maior vulnerabilidade social, para garantir coberturas vacinais equânimes e satisfatórias e reduzir o risco de reemergência da doença.

How Do Economic Fluctuations Affect the Mortality of Infectious Diseases?

This paper uses the mixed frequency vector autoregression model to explore the impact of economic fluctuations on infectious diseases mortality (IDM) from China perspective. We find that quarterly gross domestic product (GDP) fluctuations have a negative impact on the annual IDM, indicating that the mortality of infectious diseases varies counter-cyclically with the business cycle in China. Specifically, IDM usually increases with deterioration in economic conditions, and vice versa. The empirical results are consistent with the hypothesis I derived from the theoretical analysis, which highlights that economic fluctuations can negatively affect the mortality of infectious diseases. The findings can offer revelations for the government to consider the role of economic conditions in controlling the epidemic of infectious diseases. Policymakers should adopt appropriate and effective strategies to mitigate the potential negative effects of macroeconomic downturns on the mortality of infectious diseases. In the context of the COVID-19 pandemic, these analyses further emphasize the importance of promoting economic growth, increasing public health expenditure, and preventing and controlling foreign infectious diseases.

Global epidemiology and socio-economic development correlates of the reproductive ratio of COVID-19

BACKGROUND

The most commonly cited argument for imposing or lifting various restrictions in the context of the coronavirus disease 2019 (COVID-19) pandemic is an assumed impact on the reproductive ratio of the pathogen. It has furthermore been suggested that less-developed countries are particularly affected by this pandemic. Empirical evidence for this is lacking.

METHODS

Based on a dataset covering 170 countries, patterns of empirical 7-d reproductive ratios during the first months of the COVID-19 pandemic were analysed. Time trends and associations with socio-economic development indicators, such as gross domestic product per capita, physicians per population, extreme poverty prevalence and maternal mortality ratio, were analysed in mixed linear regression models using log-transformed reproductive ratios as the dependent variable.

RESULTS

Reproductive ratios during the early phase of a pandemic exhibited high fluctuations and overall strong declines. Stable estimates were observed only several weeks into the pandemic, with a median reproductive ratio of 0.96 (interquartile range 0.72-1.34) 6 weeks into the analysis period. Unfavourable socio-economic indicators showed consistent associations with higher reproductive ratios, which were elevated by a factor of 1.29 (95% confidence interval 1.15 to 1.46), for example, in the countries in the highest compared with the lowest tertile of extreme poverty prevalence.

CONCLUSIONS

The COVID-19 pandemic has allowed for the first time description of the global patterns of reproductive ratios of a novel pathogen during pandemic spread. The present study reports the first quantitative empirical evidence that COVID-19 net transmissibility remains less controlled in socio-economically disadvantaged countries, even months into the pandemic. This needs to be addressed by the global scientific community as well as international politics.

Did Economic Crisis Affect Mortality Due to Infectious Diseases? Trends of Infectious Diseases Mortality in Greece Before and After Economic Crisis

Introduction

Economic crisis and the restrictive policies applied in Greece and other countries raise questions about whether financial crises may affect the declining trend of infectious diseases. The aim of this study is to explore the impact of the economic crisis on mortality due to infectious diseases in Greece and its possible correlation with socio-economic variables affected by the crisis. 

Methods

Data including all deaths due to infectious diseases in Greece during 2001-2016 were analyzed. Annual total and cause-specific standardized death rates (SDR) and age-specific mortality rates were calculated. Cumulative SDRs and standardized rate ratios of the exposed and the non-exposed to austerity periods were computed. The correlation of mortality with Gross Domestic Product (GDP), unemployment, long-term unemployment and hospital expenditure was explored.

Results

During the exposed-to-austerity period, the SDR of infectious diseases recorded a significant increase by 5% (2.4%-7.7%), exhibiting different trends in the various groups of diseases. The cause-specific SDR increased significantly in intestinal infections, viral diseases, pneumonia, and influenza, and declined in tuberculosis and meningitis. Overall mortality was positively correlated with GDP and unemployment, and adversely with hospital expenditure.

Conclusions

The mortality of infectious disease was adversely affected by economic crisis and austerity, but the effects were found disease-dependent, with significant differences between the various groups of infectious disease. Unemployment and hospital expenditure were the main socio-economic determinants of mortality. Causal mechanisms of the impact remain unclear, requiring further research.

Big Data Analytics in the Fight against Major Public Health Incidents (Including COVID-19): A Conceptual Framework

Major public health incidents such as COVID-19 typically have characteristics of being sudden, uncertain, and hazardous. If a government can effectively accumulate big data from various sources and use appropriate analytical methods, it may quickly respond to achieve optimal public health decisions, thereby ameliorating negative impacts from a public health incident and more quickly restoring normality. Although there are many reports and studies examining how to use big data for epidemic prevention, there is still a lack of an effective review and framework of the application of big data in the fight against major public health incidents such as COVID-19, which would be a helpful reference for governments. This paper provides clear information on the characteristics of COVID-19, as well as key big data resources, big data for the visualization of pandemic prevention and control, close contact screening, online public opinion monitoring, virus host analysis, and pandemic forecast evaluation. A framework is provided as a multidimensional reference for the effective use of big data analytics technology to prevent and control epidemics (or pandemics). The challenges and suggestions with respect to applying big data for fighting COVID-19 are also discussed.

A multimethod approach for county-scale geospatial analysis of emerging infectious diseases: a cross-sectional case study of COVID-19 incidence in Germany

Background

As of 13 July 2020, 12.9 million COVID-19 cases have been reported worldwide. Prior studies have demonstrated that local socioeconomic and built environment characteristics may significantly contribute to viral transmission and incidence rates, thereby accounting for some of the spatial variation observed. Due to uncertainties, non-linearities, and multiple interaction effects observed in the associations between COVID-19 incidence and socioeconomic, infrastructural, and built environment characteristics, we present a structured multimethod approach for analysing cross-sectional incidence data within in an Exploratory Spatial Data Analysis (ESDA) framework at the NUTS3 (county) scale.

Methods

By sequentially conducting a geospatial analysis, an heuristic geographical interpretation, a Bayesian machine learning analysis, and parameterising a Generalised Additive Model (GAM), we assessed associations between incidence rates and 368 independent variables describing geographical patterns, socioeconomic risk factors, infrastructure, and features of the build environment. A spatial trend analysis and Local Indicators of Spatial Autocorrelation were used to characterise the geography of age-adjusted COVID-19 incidence rates across Germany, followed by iterative modelling using Bayesian Additive Regression Trees (BART) to identify and measure candidate explanatory variables. Partial dependence plots were derived to quantify and contextualise BART model results, followed by the parameterisation of a GAM to assess correlations.

Results

A strong south-to-north gradient of COVID-19 incidence was identified, facilitating an empirical classification of the study area into two epidemic subregions. All preliminary and final models indicated that location, densities of the built environment, and socioeconomic variables were important predictors of incidence rates in Germany. The top ten predictor variables’ partial dependence exhibited multiple non-linearities in the relationships between key predictor variables and COVID-19 incidence rates. The BART, partial dependence, and GAM results indicate that the strongest predictors of COVID-19 incidence at the county scale were related to community interconnectedness, geographical location, transportation infrastructure, and labour market structure.

Conclusions

The multimethod ESDA approach provided unique insights into spatial and aspatial non-stationarities of COVID-19 incidence in Germany. BART and GAM modelling indicated that geographical configuration, built environment densities, socioeconomic characteristics, and infrastructure all exhibit associations with COVID-19 incidence in Germany when assessed at the county scale. The results suggest that measures to implement social distancing and reduce unnecessary travel may be important methods for reducing contagion, and the authors call for further research to investigate the observed associations to inform prevention and control policy.

Association between ambient temperature and COVID-19 infection in 122 cities from China

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus.

METHODS

Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail.

RESULTS

The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses.

CONCLUSIONS

Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

Association between ambient temperature and COVID-19 infection in 122 cities from China

BACKGROUND

Coronavirus disease 2019 (COVID-19) has become a severe public health problem globally. Both epidemiological and laboratory studies have shown that ambient temperature could affect the transmission and survival of coronaviruses. This study aimed to determine whether the temperature is an essential factor in the infection caused by this novel coronavirus.

METHODS

Daily confirmed cases and meteorological factors in 122 cities were collected between January 23, 2020, to February 29, 2020. A generalized additive model (GAM) was applied to explore the nonlinear relationship between mean temperature and COVID-19 confirmed cases. We also used a piecewise linear regression to determine the relationship in detail.

RESULTS

The exposure-response curves suggested that the relationship between mean temperature and COVID-19 confirmed cases was approximately linear in the range of <3 °C and became flat above 3 °C. When mean temperature (lag0-14) was below 3 °C, each 1 °C rise was associated with a 4.861% (95% CI: 3.209-6.513) increase in the daily number of COVID-19 confirmed cases. These findings were robust in our sensitivity analyses.

CONCLUSIONS

Our results indicate that mean temperature has a positive linear relationship with the number of COVID-19 cases with a threshold of 3 °C. There is no evidence supporting that case counts of COVID-19 could decline when the weather becomes warmer, which provides useful implications for policymakers and the public.

Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models

It is well recognised that animal and plant pathogens form complex ecological communities of interacting organisms within their hosts, and there is growing interest in the health implications of such pathogen interactions. Although community ecology approaches have been used to identify pathogen interactions at the within-host scale, methodologies enabling robust identification of interactions from population-scale data such as that available from health authorities are lacking. To address this gap, we developed a statistical framework that jointly identifies interactions between multiple viruses from contemporaneous non-stationary infection time series. Our conceptual approach is derived from a Bayesian multivariate disease mapping framework. Importantly, our approach captures within- and between-year dependencies in infection risk while controlling for confounding factors such as seasonality, demographics and infection frequencies, allowing genuine pathogen interactions to be distinguished from simple correlations. We validated our framework using a broad range of synthetic data. We then applied it to diagnostic data available for five respiratory viruses co-circulating in a major urban population between 2005 and 2013: adenovirus, human coronavirus, human metapneumovirus, influenza B virus and respiratory syncytial virus. We found positive and negative covariances indicative of epidemiological interactions among specific virus pairs. This statistical framework enables a community ecology perspective to be applied to infectious disease epidemiology with important utility for public health planning and preparedness.

Disease-causing microorganisms, including viruses, bacteria, protozoa and fungi, form complex communities within animals and plants. These microorganisms can coexist harmoniously or even beneficially, or they may competitively interact for host resources. Well-studied examples include interactions between viruses and bacteria in the respiratory tract. Whilst ecological studies have revealed that some pathogens do interact within their hosts, identifying interactions from available population scale data from health authorities is challenging. This is exacerbated by a lack of large-scale data describing the infection patterns of multiple pathogens within single populations over long time frames. Furthermore, methods for evaluating whether infection frequencies of different pathogens fluctuate together or not over time cannot readily account for alternative explanations. For example, human pathogens may have related seasonal patterns depending on the age groups they infect and the weather conditions they survive in, and not because they are interacting. We developed a robust statistical framework to identify pathogen-pathogen interactions from population scale diagnostic data. This framework serves as a crucial step in identifying such important interactions and will guide new studies to elucidate their underpinning mechanisms. This will have important consequences for public health preparedness and the design of effective disease control interventions.