Background: The novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2, also called 2019-nCoV) causes different morbidity risks to individuals in different age groups. This study attempts to quantify the age-specific transmissibility using a mathematical model. Methods: An epidemiological model with five compartments (susceptible–exposed–symptomatic–asymptomatic–recovered/removed [SEIAR]) was developed based on observed transmission features. Coronavirus disease 2019 (COVID-19) cases were divided into four age groups: group 1, those ≤ 14 years old; group 2, those 15 to 44 years old; group 3, those 45 to 64 years old; and group 4, those ≥ 65 years old. The model was initially based on cases (including imported cases and secondary cases) collected in Hunan Province from January 5 to February 19, 2020. Another dataset, from Jilin Province, was used to test the model. Results: The age-specific SEIAR model fitted the data well in each age group (P < 0.001). In Hunan Province, the highest transmissibility was from age group 4 to 3 (median: β₄₃ = 7.71 × 10^− 9; SAR₄₃ = 3.86 × 10^− 8), followed by group 3 to 4 (median: β₃₄ = 3.07 × 10^− 9; SAR₃₄ = 1.53 × 10^− 8), group 2 to 2 (median: β₂₂ = 1.24 × 10^− 9; SAR₂₂ = 6.21 × 10^− 9), and group 3 to 1 (median: β₃₁ = 4.10 × 10^− 10; SAR₃₁ = 2.08 × 10^− 9). The lowest transmissibility was from age group 3 to 3 (median: β₃₃ = 1.64 × 10^− 19; SAR₃₃ = 8.19 × 10^− 19), followed by group 4 to 4 (median: β₄₄ = 3.66 × 10^− 17; SAR₄₄ = 1.83 × 10^− 16), group 3 to 2 (median: β₃₂ = 1.21 × 10^− 16; SAR₃₂ = 6.06 × 10^− 16), and group 1 to 4 (median: β₁₄ = 7.20 × 10^− 14; SAR₁₄ = 3.60 × 10^− 13). In Jilin Province, the highest transmissibility occurred from age group 4 to 4 (median: β₄₃ = 4.27 × 10^− 8; SAR₄₃ = 2.13 × 10^− 7), followed by group 3 to 4 (median: β₃₄ = 1.81 × 10^− 8; SAR₃₄ = 9.03 × 10^− 8). Conclusions: SARS-CoV-2 exhibits high transmissibility between middle-aged (45 to 64 years old) and elderly (≥ 65 years old) people. Children (≤ 14 years old) have very low susceptibility to COVID-19. This study will improve our understanding of the transmission feature of SARS-CoV-2 in different age groups and suggest the most prevention measures should be applied to middle-aged and elderly people.

Background: Several studies have investigated the associations between ambient temperature and years of life lost (YLLs), but few focused on the difference of life loss attributable to temperature among different socioeconomic development levels. Objectives: We investigated the disparity in temperature-YLL rate relationships and life loss per death attributable to nonoptimal temperature in regions with various development levels. Methods: Three hundred sixty-four Chinese counties or districts were classified into 92 high-development regions (HDRs) and 272 low-development regions (LDRs) according to socioeconomic factors of each location using K-means clustering approach. We used distributed lag non-linear models (DLNM) and multivariate meta-analysis to estimate the temperature-YLL rate relationships. We calculated attributable fraction (AF) of YLL and temperature-related average life loss per death to compare mortality burden of temperature between HDRs and LDRs. Stratified analyses were conducted by region, age, sex and cause of death. Results: We found that non-optimal temperatures increased YLL rates in both HDRs and LDRs, but all subgroups in LDRs were more vulnerable. The disparity of cold effects between HDRs and LDRs was significant, while the difference in heat effect was insignificant. The overall AF of non-optimal temperature in LDRs [AF = 12.2, 95% empirical confidence interval (eCI):11.0–13.5%] was higher than that in HDRs (AF = 8.9, 95% eCI: 8.3–9.5%). Subgroups analyses found that most groups in LDRs had greater AFs than that in HDRs. The average life loss per death due to non-optimal temperature in LDRs (1.91 years, 95% eCI: 1.72–2.10) was also higher than that in HDRs (1.32 years, 95% eCI: 1.23–1.41). Most of AFs and life loss per death were caused by moderate cold in both HDRs and LDRs. Conclusions: Mortality burden caused by temperature was more significant in LDRs than that in HDRs, which means that more attention should be paid to vulnerable populations in LDRs in planning adaptive strategies.

Background: Based on differences in populations and prevention and control measures, the spread of new coronary pneumonia in different countries and regions also differs. This study aimed to calculate the transmissibility of coronavirus disease 2019 (COVID-19), and to evaluate the effectiveness of measures to control the disease in Jilin Province, China. Methods: The data of reported COVID-19 cases were collected, including imported and local cases from Jilin Province as of March 14, 2019. A Susceptible–Exposed–Infectious–Asymptomatic–Recovered/Removed (SEIAR) model was developed to fit the data, and the effective reproduction number (R_eff) was calculated at different stages in the province. Finally, the effectiveness of the measures was assessed. Results: A total of 97 COVID-19 infections were reported in Jilin Province, among which 45 were imported infections (including one asymptomatic infection) and 52 were local infections (including three asymptomatic infections). The model fit the reported data well (R² = 0.593, P < 0.001). The R_eff of COVID-19 before and after February 1, 2020 was 1.64 and 0.05, respectively. Without the intervention taken on February 1, 2020, the predicted cases would have reached a peak of 177,011 on October 22, 2020 (284 days from the first case). The projected number of cases until the end of the outbreak (on October 9, 2021) would have been 17,129,367, with a total attack rate of 63.66%. Based on the comparison between the predicted incidence of the model and the actual incidence, the comprehensive intervention measures implemented in Jilin Province on February 1 reduced the incidence of cases by 99.99%. Therefore, according to the current measures and implementation efforts, Jilin Province can achieve good control of the virus’s spread. Conclusions: COVID-19 has a moderate transmissibility in Jilin Province, China. The interventions implemented in the province had proven effective; increasing social distancing and a rapid response by the prevention and control system will help control the spread of the disease.

Background: The disease burden of hepatitis E remains high. We used a new method (richness, diversity, evenness, and similarity analyses) to classify cities according to the occupational classification of hepatitis E patients across regions in China and compared the results of cluster analysis.Methods: Data on reported hepatitis E cases from 2008 to 2018 were collected from 24 cities (9 in Jilin Province, 13 in Jiangsu Province, Xiamen City, and Chuxiong Yi Autonomous Prefecture). Traditional statistical methods were used to describe the epidemiological characteristics of hepatitis E patients, while the new method and cluster analysis were used to classify the cities by analyzing the occupational composition across regions.Results: The prevalence of hepatitis E in eastern China (Jiangsu Province) was similar to that in the south (Xiamen City) and southwest of China (Chuxiong Yi Autonomous Prefecture), but higher than that in the north (Jilin Province). The age of hepatitis E patients was concentrated between 41 and 60 years, and the sex ratio ranged from 1:1.6 to 1:3.4. Farming was the most highly prevalent occupation; other sub-prevalent occupations included retirement, housework and unemployment. The incidence of occupations among migrant workers, medical staff, teachers, and students was moderate. There were several occupational types with few or no records, such as catering industry, caregivers and babysitters, diaspora children, childcare, herders, and fishing (boat) people. The occupational similarity of hepatitis E was high among economically developed cities, such as Nanjing, Wuxi, Baicheng, and Xiamen, while the similarity was small among cities with large economic disparities, such as Nanjing and Chuxiong Yi Autonomous Prefecture. A comparison of the classification results revealed more similarities and some differences when using these two methods.Conclusion: In China, the factors with the greatest influence on the prevalence of hepatitis E are living in the south, farming as an occupation, being middle-aged or elderly, and being male. The 24 cities we studied were highly diverse and moderately similar in terms of the occupational distribution of patients with hepatitis E. We confirmed the validity of the new method on in classifying cities according to their occupational composition by comparing it with the clustering method.

Bacterial dysentery (BD) brings a major disease burden to developing countries. Exploring the influence of temperature and its interaction with other meteorological factors on BD is significant for the prevention and early warning of BD in the context of climate change. Daily BD cases and meteorological data from 2008 to 2018 were collected in all nine prefecture-level cities in Jilin Province. A one-stage province-level model and a two-stage city-specific multivariate meta-pooled level distributed lag non-linear model were established to explore the correlation between temperature and BD, then the weather-stratified generalised additive model was used to test the interaction. During the study period, a total of 26 971 cases of BD were developed. The one-stage and two-stage cumulative dose-response ‘J’ curves overlapped, and results showed a positive correlation between temperature and BD with a 1–6 days lag effect. Age group ⩾5 years was found to be more sensitive to the effects. Moreover, there was a significant interaction between temperature, humidity and precipitation (P = 0.004, 0.002, respectively) on BD under high temperature (>0 °C), reminding residents and policymakers to pay attention to the prevention of BD in situations with both high temperature and humidity, high temperature and precipitation during the temperate monsoon climate.

Background: There is still a relatively serious disease burden of infectious diseases and the warning time for different infectious diseases before implementation of interventions is important. The logistic differential equation models can be used for predicting early warning of infectious diseases. The aim of this study is to compare the disease fitting effects of the logistic differential equation (LDE) model and the generalized logistic differential equation (GLDE) model for the first time using data on multiple infectious diseases in Jilin Province and to calculate the early warning signals for different types of infectious diseases using these two models in Jilin Province to solve the disease early warning schedule for Jilin Province throughout the year. Methods: Collecting the incidence of 22 infectious diseases in Jilin Province, China. The LDE and GLDE models were used to calculate the recommended warning week (RWW), the epidemic acceleration week (EAW) and warning removed week (WRW) for acute infectious diseases with seasonality, respectively. Results: Five diseases were selected for analysis based on screening principles: hemorrhagic fever with renal syndrome (HFRS), shigellosis, mumps, Hand, foot and mouth disease (HFMD), and scarlet fever. The GLDE model fitted the above diseases better (0.80 ≤ R² ≤ 0.94, P < 0. 005) than the LDE model. The estimated warning durations (per year) of the LDE model for the above diseases were: weeks 12–23 and 40–50; weeks 20–36; weeks 15–24 and 43–52; weeks 26–34; and weeks 16–25 and 41–50. While the durations of early warning (per year) estimated by the GLDE model were: weeks 7–24 and 36–51; weeks 13–37; weeks 11–26 and 39–54; weeks 23–35; and weeks 12–26 and 40–50. Conclusions: Compared to the LDE model, the GLDE model provides a better fit to the actual disease incidence data. The RWW appeared to be earlier when estimated with the GLDE model than the LDE model. In addition, the WRW estimated with the GLDE model were more lagged and had a longer warning time.