Quantifying COVID-19 importation risk in a dynamic network of domestic cities and international countries.

Since its outbreak in December 2019, the novel coronavirus 2019 (COVID-19) has spread to 191 countries and caused millions of deaths. Many countries have experienced multiple epidemic waves and faced containment pressures from both domestic and international transmission. In this study, we conduct a multiscale geographic analysis of the spread of COVID-19 in a policy-influenced dynamic network to quantify COVID-19 importation risk under different policy scenarios using evidence from China. Our spatial dynamic panel data (SDPD) model explicitly distinguishes the effects of travel flows from the effects of transmissibility within cities, across cities, and across national borders. We find that within-city transmission was the dominant transmission mechanism in China at the beginning of the outbreak and that all domestic transmission mechanisms were muted or significantly weakened before importation posed a threat. We identify effective containment policies by matching the change points of domestic and importation transmissibility parameters to the timing of various interventions. Our simulations suggest that importation risk is limited when domestic transmission is under control, but that cumulative cases would have been almost 13 times higher if domestic transmissibility had resurged to its precontainment level after importation and 32 times higher if domestic transmissibility had remained at its precontainment level since the outbreak. Our findings provide practical insights into infectious disease containment and call for collaborative and coordinated global suppression efforts.

Uncovering the drivers of CO2 emissions in the United States: The hidden spillover effects.

This study explores the applicability of the Environmental Kuznets Curve (EKC) hypothesis in the United States (US) from 2006 to 2020, employing the Spatial Durbin Model (SDM) to analyze the cross-border effects of pollution among states. The results indicate that although economic growth initially decreases environmental degradation, it subsequently contributes to more significant environmental degradation, challenging the EKC hypothesis's validity at the US state level. Factors such as higher energy prices and reliance on fossil fuels are also identified as significant drivers of environmental deterioration, with varying impacts observed across states. Conversely, adopting renewable energy sources is crucial in mitigating pollution levels. The study underscores the importance of coordinated state-level efforts to harmonize economic growth with sustainable environmental practices. It highlights the complexities of policymaking in balancing economic development with environmental conservation and emphasizes the need for targeted interventions to address environmental challenges effectively. This research enhances our understanding of sustainable development pathways amidst diverse regional dynamics within the US by providing empirical evidence and policy insights.

The global burden of schizophrenia and the impact of urbanization during 1990-2019: An analysis of the global burden of disease study 2019.

BACKGROUND AND HYPOTHESIS: The burden of schizophrenia is increasing. Assessing the global distribution of schizophrenia and understanding the association between urbanization factors and schizophrenia are crucial. STUDY DESIGN: We conducted a two-stage analysis utilizing public data from GBD (global burden of disease) 2019 and the World Bank. First, the distribution of schizophrenia burden at the global, regional, and national levels as well as temporal trends was analyzed. Then, four composite indicators of urbanization (including demographic, spatial, economic, and eco-environment urbanization) were constructed from ten basic indicators. Panel data models were used to explore the relationship between urbanization indicators and the burden of schizophrenia. RESULTS: In 2019, there were 23.6 million people with schizophrenia, an increase of 65.85% from 1990, and the country with the largest ASDR (age-standardized disability adjusted life years rate) was the United States of America, followed by Australia, and New Zealand. Globally, the ASDR of schizophrenia rose with the sociodemographic index (SDI). In addition, six basic urbanization indicators including urban population proportion, employment in industry/services proportion, urban population density, the population proportion in the largest city, GDP, and PM2.5 concentration were positively associated with ASDR of schizophrenia, with the largest coefficients being urban population density. Overall, demographic, spatial, economic, and eco-environment urbanization all had positive effects on schizophrenia, and the estimated coefficients indicated that demographic urbanization was the most significant influence. CONCLUSIONS: This study provided a comprehensive description of the global burden of schizophrenia and explored urbanization as a factor contributing to the variation in the burden of schizophrenia, and highlighted policy priorities for schizophrenia prevention in the context of urbanization.

Estimation and model selection in general spatial dynamic panel data models.

Commonly used methods for estimating parameters of a spatial dynamic panel data model include the two-stage least squares, quasi-maximum likelihood, and generalized moments. In this paper, we present an approach that uses the eigenvalues and eigenvectors of a spatial weight matrix to directly construct consistent least-squares estimators of parameters of a general spatial dynamic panel data model. The proposed methodology is conceptually simple and efficient and can be easily implemented. We show that the proposed parameter estimators are consistent and asymptotically normally distributed under mild conditions. We demonstrate the superior performance of our approach via extensive simulation studies. We also provide a real data example.

Global human obesity and political globalization; asymmetric relationship through world human development levels.

Purpose - Political globalization is a crucial and distinct component of strengthening global organizations. Obesity is a global epidemic in a few nations, and it is on the verge of becoming a pandemic that would bring plenty of diseases. This research aims to see how the political globalization index affects worldwide human obesity concerning global human development levels. Methods- To assess any cross-sectional dependence among observed 109 nations, the yearly period from 1990 to 2017 is analyzed using second generation panel data methods. KAO panel cointegration test and Fully Modified Least Square model were used to meet our objectives. Finding- Low level of political globalization tends to increase global human obesity because countries cannot sway international decisions and resources towards them. While the high level of political globalization tends to reduce obesity because it can control and amends international decisions. For the regression model, a fully modified Least Square model was utilized. The study observed that the R squared values for all models are healthy, with a minimum of 87 percent variables explaining differences in global obesity at the country level. Originality: There is very important to tackle the globalization issue to reduce global human obesity. With the simplicity of dietary options and the amount of physical labour they undergo in their agricultural duties, an increase in rural population percentage tends to lower the average national obesity value.

Migration and Democratization in Brazil: The Case of Electoral Participation and Competition.

Deepening democratization in Brazil has coincided with sustained flows of domestic migration, which raises an important question of whether migration deepens or depresses democratic development in migrant-sending regions. Whereas earlier perspectives have viewed migration as a political "brain drain," we contend that out-migration can generate resources that promote democratic processes back home. We investigate the role of migration in two aspects of democratization: electoral participation and competition. The analyses are based on spatial panel data models of mayoral election results across all municipalities between 1996 and 2012. The results show that migration increases electoral participation and competition in migrant-sending localities in Brazil. This study also identifies the sociopolitical context that conditions the impact of migration: the effect is most often present in the context of rural-urban migration and is more pronounced in sending localities with less democratic political structures. Moreover, using spatial network models, we find evidence for the transmission of political remittances from migration destination municipalities to origin municipalities. The present study extends the research on the migration-development nexus to the political arena, thus demonstrating the value of integrating demographic processes into explanations of political change.

Seeking sustainable development policies at the municipal level based on the triad of city, economy and environment: evidence from Hunan province, China.

Keeping urbanization, economy and eco-environment in harmony is a core issue for attaining Sustainable Development Goals (SDGs) in any complex geographical regions. Previous studies mainly focused on seeking the balance between urban expansion levels, eco-environment quality and socioeconomic degree. But the challenges still exist in solving the negative influence of urban expansion that affects eco-environmental and economic development. Based on the Environmental Kuznets Curve theory, we involved inclusive indexes to analyze the interlinkages of eco-environment quality, economic level, and urban expansion degree, which closely relate to urban sustainable development goals and spatial complexity, as well as using available data corresponding to waterfront cities. Cities in Hunan were taken as a study-case, and the study period of 2006-2016 covers the last 10 years of the millennium development goals agenda and the first 2 years of SDGs agenda. The key indicators of city-economy-environment relationships were different at the provincial level, urban level and urbanization grade. According to the regression models and inverted N shape curve, urban expansion resulted in high positive effects on economic development level and negative effects on ecological environment quality, partically higher at high urbanization level than that of the low ones. But the overall trends were that the environmental quality of the cities was undergoing slowly improving processes both at low and high urban expansion levels. Promoting adaptations with the eco-environmental capacity when formulating policies and taking actions is necessary for realizing sustainable cities and communities (SDG11), life on land (SDG15), decent work and economic growth (SDG8) and responsible consumption and production (SDG12) at the same time. Regulating citizens' density, urban expansion speed in area, the quantity of enterprises with heavy pollution, and the structure of industry to the suitable urbanization stages is an important way for achieving SDGs at provincial and municipal levels.

Green infrastructure for coastal flood protection: The longitudinal impacts of green infrastructure patterns on flood damage.

The importance of implementing green infrastructure (GI) for flood protection is supported by multiple substantial cross-sectional analyses. Yet, limited longitudinal research has been conducted which addresses how to maintain and improve the configuration of GI in order to minimize the cost of losses resulting from flooding. Structural damage from devastating storm events has repeatedly imposed substantial financial burdens on local governments in coastal regions. This study longitudinally examines the impacts of changes in GI patterns on flood damage cost in coastal Texas areas. Major flood events in the 36 Texan coastal watershed counties along the Gulf of Mexico were monitored from 2000 to 2017. Along with non-spatially weighted panel data models, we developed an advanced statistical model controlling for spatially correlated errors in flood loss and predicting flood loss with a set of time-series socioeconomic and environmental control variables. The results of the spatial panel data model reveal that long-term maintenance of larger, more irregular, more dispersed, less fragmented, and less connected patterns of GI will help to reduce county-level flood damage costs per capita over time. Most importantly, protecting larger patches within a closer proximity was found to be of the utmost importance for retaining the flood regulation services provided by GI. These findings suggest that planners and natural resource managers should enhance supportive land use policies to preserve existing GI and strategically locate new implementations in order to achieve long-term flood protection.

Dynamics, differences, influencing factors of eco-efficiency in China: A spatiotemporal perspective analysis.

Eco-efficiency can effectively measure the relationship between economy, resources, environment, and development. Understanding eco-efficiency is of great practical significance for decision makers tasked with addressing and delivering sustainable socioeconomic development. Based on an "undesirable output Slacks-Based Measure models," this study evaluated the eco-efficiency of 285 Chinese cities during the period 2004-2014, analyzing spatiotemporal dynamics and influencing factors using a Spatial Autocorrelation Panel Data Model. The main results are as follows: From the spatial perspective, eco-efficiency in Chinese cities generally evidenced an M-shaped trend (increasing-decreasing-increasing-decreasing), and imbalanced spatiotemporal dynamics. Furthermore, the urban eco-efficiency generally presented a distinct convergence of HH cluster and LL cluster clubs, with the latter being the most dominant. From the regional point of view, there existed a decreasing trend in the efficiencies of cities, from eastern China to central and western China. In addition, we identified significant differences in the eco-efficiency of different cities in China, and the coefficient of variation of eco-efficiency showed a general decrease. The results of our estimation of the factors affecting urban eco-efficiency showed that the economic development level, the industrial structure, import and export trade, and the information level all had significant positive influence, and local government expenditure, social retail sales of consumer goods, and infrastructure all had a negative effect on urban eco-efficiency. This paper puts forward numbers of suggestions for ways to promote social, ecological, and economic development in Chinese cities, based on our findings.

What is the health cost of haze pollution? Evidence from China.

INTRODUCTION: Haze pollution has become a serious threat to the health of residents and has brought about considerable economic costs. The objectives of this study are to examine the relationship between haze pollution and residents' health and to estimate the health cost of haze pollution. METHODS: Using macro data on 74 major Chinese cities, this study employs a static panel data model and a dynamic panel generalized method of moments model to investigate the impact of haze pollution on the mortality of residents. On the basis of the estimation results, the value of a statistical life (VOSL) method is used to evaluate the economic cost of haze pollution on residents' health. RESULTS: In 74 major cities of China, an increase in concentration of PM2.5 pollutants by 1 µg/m3 may cause the mortality rate to rise by 25 per million people. The VOSL of haze pollution in China is about 1.53 million yuan. The health cost of residents from haze in 74 major cities of China in 2016 was about 888 billion yuan, equivalent to about 2% of the these cities' GDP. CONCLUSIONS: Haze pollution has serious damage to the health of urban residents and therefore brings about considerable economic losses.

Will regional economic integration influence carbon dioxide marginal abatement costs? Evidence from Chinese panel data.

With frequent trade and technology diffusion, the barriers between regions are gradually weakening, and regions have become more integrated over recent years. Regional economic integration not only stimulates labour mobility, but also achieve scale economy, both of which may also influence carbon dioxide (CO2) marginal abatement costs through affecting energy consumption, CO2 emissions, productivity growth, and technical progress. Nevertheless, to the best of our knowledge, none of the studies has currently concerned the influence of regional economic integration on CO2 marginal abatement costs. To fill this research gap, this study first theoretically clarifies the influence mechanism of regional economic integration on CO2 marginal abatement cost, and then empirically attempts to investigate their relationship in the context of China, with panel data models. To serve this purpose, the provincial CO2 marginal abatement cost and regional economic integration are estimated by parametric directional distance function and price-based approach, respectively. The results show that China's regional economic integration level indeed gradually improved over 2002-2011 except in 2003-04 and 2006-09 due to the spread of Severe Acute Respiratory Syndrome (SARS) and the sub-prime loan crisis. Moreover, evolution of regional economic integration indeed contributes to the increase of CO2 marginal abatement cost at 5% significance level. Using robust tests, it can be found that the results are also reliable and robust to sub-samples.

The Ambiguous Effect of GP Competition: The Case of Hospital Admissions.

In the theoretical literature on general practitioner (GP) behaviour, one prediction is that intensified competition induces GPs to provide more services resulting in fewer hospital admissions. This potential substitution effect has drawn political attention in countries looking for measures to reduce the growth in demand for hospital care. However, intensified competition may induce GPs to secure hospital admissions a signal to attract new patients and to keep the already enlisted ones satisfied, resulting in higher admission rates at hospitals. Using both static and dynamic panel data models, we aim to enhance the understanding of whether such relations are causal. Results based on ordinary least square (OLS) models indicate that aggregate inpatient admissions are negatively associated with intensified competition both in the full sample and for the sub-sample patients aged 45 to 69, while outpatient admissions are positively associated. Fixed-effect estimations do not confirm these results though. However, estimations of dynamic models show significant negative (positive) effects of GP competition on aggregate inpatient (outpatient) admissions in the full sample and negative effects on aggregate inpatient admissions and emergency admissions for the sub-sample. Thus, intensified GP competition may reduce inpatient hospital admissions by inducing GPs to provide more services, whereas, the alternative hypothesis seems valid for outpatient admissions. © 2016 The Authors. Health Economics Published by John Wiley & Sons, Ltd.

Different effects of meteorological factors on hand, foot and mouth disease in various climates: a spatial panel data model analysis.

BACKGROUND: Major outbreaks of hand, foot and mouth disease (HFMD) have been reported in China since 2008, posing a great threat to the health of children. Although many studies have examined the effect of meteorological variables on the incidence of HFMD, the results have been inconsistent. This study aimed to quantify the relationship between meteorological factors and HFMD occurrence in different climates of mainland China using spatial panel data models. METHODS: All statistical analyses were carried out according to different climate types. We firstly conducted a descriptive analysis to summarize the epidemic characteristics of HFMD from May 2008 to November 2012 and then detected the spatial autocorrelation of HFMD using a global autocorrelation statistic (Moran's I) in each month. Finally, the association between HFMD incidence and meteorological factors was explored by spatial panel data models. RESULTS: The 353 regions were divided into 4 groups according to climate (G1: subtropical monsoon climate; G2: temperate monsoon climate; G3: temperate continental climate; G4: plateau mountain climate). The Moran's I values were significant with high correlations in most months of group G1 and G2 and some months of group G3 and G4. This suggested the existence of a high spatial autocorrelation with HFMD. Spatial panel data models were more appropriate to describe the data than fixed effect models. The results showed that HFMD incidences were significantly associated with average atmospheric pressure (AAP), average temperature (AT), average vapor pressure (AVP), average relative humidity (ARH), monthly precipitation (MP), average wind speed (AWS), monthly total sunshine hours (MSH), mean temperature difference (MTD), rain day (RD) and average temperature distance (ATD), but the effect of meteorological factors might differ in various climate types. CONCLUSIONS: Spatial panel data models are useful and effective when longitudinal data are available and spatial autocorrelation exists. Our findings showed that meteorological factors were related to the occurrence of HFMD, which were also affected by climate type.

Determinants of maternal mortality in Eastern Mediterranean region: A panel data analysis.

BACKGROUND: As one of the main criteria of health outcomes, maternal mortality indicates the socioeconomic development level of countries. The present study aimed at identifying and analyzing the effective factors on maternal mortality in Eastern Mediterranean Region (EMR) of the World Health Organization (WHO). METHODS: Analytical model was developed based on the literature review. Panel data of 2004-2011 periods for 22 EMR countries was used. Required data were collected from WHO online database. Based on results of diagnostic tests for panel data model, parameters of model were estimated by fixed effects method. RESULTS: Descriptive statistics demonstrated the large disparities in social, economic, and health indicators among EMRO countries. Findings obtained from evaluating the model showed a negative, significant relationship between GDP per capita (ß=-0.869, p<0.01), health expenditure) ß=-0.525, p<0.01 (female literacy rate) ß=-1.045, <0.01 (skilled birth attendance) ß=-0.899, p<0.05) and maternal mortality rate. CONCLUSION: Improved income and economic development, increased resources allocated to the health sector, improved delivery services particularly the increased use of trained staff in the delivery, improve quality of primary care centers, mitigating the risks of marginalization and its dangers, and especially improving the level of women's education and knowledge are the key factors in policy making related to maternal health promotion.

Environmental costs and renewable energy: re-visiting the Environmental Kuznets Curve.

The environmental costs of economic development have received increasing attention during the last years. According to the World Energy Outlook (2013) sustainable energy policies should be promoted in order to spur economic growth and environmental protection in a global context, particularly in terms of reducing greenhouse gas emissions that contribute to climate change. Within this framework, the European Union aims to achieve the "20-20-20" targets, including a 20% reduction in EU greenhouse gas emissions from 1990 levels, a raise in the share of EU energy consumption produced from renewable resources to 20% and a 20% improvement in the EU's energy efficiency. Furthermore, the EU "Energy Roadmap 2050" has been recently adopted as a basis for developing a long-term European energy framework, fighting against climate change through the implementation of energy efficiency measures and the reduction of emissions. This paper focuses on the European context and attempts to explain the impact of economic growth on CO2 emissions through the estimation of an Environmental Kuznets Curve (EKC) using panel data. Moreover, since energy seems to be at the heart of the environmental problem it should also form the core of the solution, and therefore we provide some extensions of the EKC by including renewable energy sources as explanatory variables in the proposed models. Our data sets are referred to the 27 countries of the European Union during the period 1996-2010. With this information, our empirical results provide some interesting evidence about the significant impacts of renewable energies on CO2 emissions, suggesting the existence of an extended EKC.

Slowing the expansion of impervious surfaces: the key to promoting high-quality and sustainable regional development.

Sponge cities are disaster-resilient and sustainable infrastructure, and the emergence of impervious surfaces hinders the construction of sponge cities. In response to the United Nations' 2030 Sustainable Development Goals, the study of the spatio-temporal evolution and influencing factors of impervious surfaces provides an effective basis for the construction of sponge cities. In this paper, multi-source remote sensing images (Landsat 4-5 TM was used in 2000 and 2010, Landsat 8 OLI/TIRS was used in 2020) were used as data sources to construct a multi-feature impervious surface estimation model. By combining and refining the advantages of MISI, NDBI, and BUAI feature indices, we obtained the impervious surface cover of Nanchang City in 2000, 2010, and 2020. And its spatio-temporal evolution characteristics were analyzed by using the ESDA. The main factors of the impervious surface were analyzed by using the ordinary panel data model and the spatial durbin model. Results: (1) From 2000 to 2020, the impervious surface area of Nanchang City increased from 516.13 to 1075.12 km2. The overall impervious surface distribution in Nanchang City expressed a significant neighborhood distribution; (2) socio-economic factors had a positive role in promoting the impervious surface of Nanchang City. Among them, the correlation coefficient of the economic development index (0.2332), real estate investment (0.1518), and gross industrial output value (0.0453) were the most significant in the local areas; (3) the economic development index (0.2307), real estate investment (0.0251), and passenger volume (0.1679) stimulated the growth of impervious surfaces in adjacent areas, and the total population (-0.8074) had a buffering effect on adjacent areas. In order to promote the sustainable development of the region, it is necessary to propose corresponding measures and suggestions based on the impervious surface of Nanchang City.

Impact of economic development on soil trace metal(loid)s pollution: A case study of China.

Recently, intensive anthropogenic activities, while promoting economic growth, have also exacerbated soil trace metal(loid) (TM) pollution. To explore the impact of economic development on soil TM pollution, a time-weighted method was introduced to calculate the average concentrations of eight TMs in Chinese topsoil from 2001 to 2020, and panel data on TMs and economic factors of 31 provinces were used for regression analysis. The results revealed that the average concentrations of soil TMs all exceeded their respective soil background values. Meanwhile, the spatial distribution of soil TMs was characterized by obvious regional heterogeneity, with economically developed areas being heavily polluted and having high ecological risks. In addition, the results derived from panel data models showed that the relationship between soil TM pollution and economic development in China presented a continuous growth curve, but with an N-shaped pattern in eastern China, a U-shaped pattern in central China, and a positive linearity in western China. Four control variables were also introduced to evaluate their impact on TM pollution, and the results indicated that the proportion of secondary industry and the road area per capita were the major influencing factors. Ultimately, the inflection point estimation results suggested that the soil TM pollution level will increase in eastern China, central China and western China with ongoing economic growth. Our findings contribute to the current understanding of the relationship between soil TM pollution and anthropogenic activities, and provide a scientific basis for adjusting and planning industrial development and layout according to the characteristics of soil TM pollution.

How urbanization affects residents' health risks: evidence from China.

Continued urbanization requires a deep understanding of how urbanization affects residents' health risks. This study used regression analysis of Chinese provincial-level panel data from 2004 to 2019 and empirically analyzed the nonlinear effects of urbanization on health risks and regional differences using STIRPAT model. Health risks were assessed by the average number of residents' visits to medical facilities and population mortality. We also examined the moderating effect of income and environmental factors. The results show that (1) urbanization increases the average number of residents' visits and reduces population mortality. The positive effect of urbanization in increasing the average number of visits is reinforced by an increase in income level and environmental pollution, whereas the negative effect of urbanization in reducing population mortality is weakened by environmental pollution. (2) Regarding long-term trends, urbanization has an N-shaped relationship with the average number of residents' visits, and a U-shaped relationship with population mortality; (3) Urbanization has an N-shaped relationship with the average number of residents' visits in the eastern, central, and western regions and an inverted N-shaped relationship with population mortality in the eastern region. Urbanization has significant effects on residents' health risks in areas with high levels of infrastructure. According to the results, suggestions are proposed, such as developing new-type urbanization, improving infrastructure, focusing on green urbanization, and promoting national fitness programs.

Speed of convergence in global value chains: Forward or backward linkage.

The COVID-19 pandemic is creating uncertainty that potentially causes a decline in global trade. It hampers the catching-up processes of developing countries because they are already connected through global value chains (GVCs). This study investigates the speed of convergence in GVC participation. Furthermore, we propose a forward and backward GVC participation approach to trace domestic and foreign value-added contributions and to overcome overvaluation in gross exports. We compare forward and backward linkages and determine countries' convergence speeds. We scrutinize stochastic, σ, and ß-convergence using the system generalized method of moments; additionally, we use this method to address potential endogeneity issues. Using a dynamic panel approach, we investigate global convergence countries in GVCs and possible foreign direct investment (FDI) factors that may influence convergence. In addition, we analyze the early effect of the COVID-19 pandemic on the convergence rate using the recent Asian Development Bank multi-regional input-output database that covers the period 2010-2019. The results illustrate convergence in both forward and backward GVC participation; however, the convergence speed varies among group countries. A group comprising countries in the Association of Southeast Asian Nations and other countries experiences faster convergence than advanced countries in forward GVC participation. In contrast, advanced countries experience faster convergence in backward GVC participation. Furthermore, the results reveal that FDI plays a significant role in GVC participation. Overall, The COVID-19 pandemic has decreased GVC participation on average.

Detrended seasonal relationships and impact of climatic factors combined with spatiotemporal effect on the prevalence of human brucellosis.

Human brucellosis (HB) is a seasonal and climate-affected infectious disease that is posing an increasing threat to public health and economy. However, most of the research on the seasonal relationships and impact of climatic factors on HB did not consider the secular trend and spatiotemporal effect related to the disease. We herein utilized long-term surveillance data on HB from 2008 to 2020 using sinusoidal models to explore detrended relationships between climatic factors and HB. In addition, we assessed the impact of such climatic factors on HB using a spatial panel data model combined with the spatiotemporal effect. HB peaked around mid-May. HB was significantly correlated with climatic factors with 1-5-month lag when the respective correlations reached the maximum across the different lag periods. Each 0.1 °C increase in temperature led to 0.5% decrease in the 5-month lag incidence of HB. We also observed a positive spatiotemporal effect on the disease. Our study provides a detailed and in-depth overview of seasonal relationships and impact of climatic factors on HB. In addition, it proposes a novel approach for exploring the seasonal relationships and quantifying the impacts of climatic factors on various infectious diseases.