Relative transmissibility of hand, foot and mouth disease from male to female individuals

Assessing the Impacts of Meteorological Factors on COVID-19 Pandemic Using Generalized Estimating Equations

Background

Meteorological factors have been proven to affect pathogens; both the transmission routes and other intermediate. Many studies have worked on assessing how those meteorological factors would influence the transmissibility of COVID-19. In this study, we used generalized estimating equations to evaluate the impact of meteorological factors on Coronavirus disease 2019 (COVID-19) by using three outcome variables, which are transmissibility, incidence rate, and the number of reported cases.

Methods

In this study, the data on the daily number of new cases and deaths of COVID-19 in 30 provinces and cities nationwide were obtained from the provincial and municipal health committees, while the data from 682 conventional weather stations in the selected provinces and cities were obtained from the website of the China Meteorological Administration. We built a Susceptible-Exposed-Symptomatic-Asymptomatic-Recovered/Removed (SEIAR) model to fit the data, then we calculated the transmissibility of COVID-19 using an indicator of the effective reproduction number (Reff ). To quantify the different impacts of meteorological factors on several outcome variables including transmissibility, incidence rate, and the number of reported cases of COVID-19, we collected panel data and used generalized estimating equations. We also explored whether there is a lag effect and the different times of meteorological factors on the three outcome variables.

Results

Precipitation and wind speed had a negative effect on transmissibility, incidence rate, and the number of reported cases, while humidity had a positive effect on them. The higher the temperature, the lower the transmissibility. The temperature had a lag effect on the incidence rate, while the remaining five meteorological factors had immediate and lag effects on the incidence rate and the number of reported cases.

Conclusion

Meteorological factors had similar effects on incidence rate and number of reported cases, but different effects on transmissibility. Temperature, relative humidity, precipitation, sunshine hours, and wind speed had immediate and lag effects on transmissibility, but with different lag times. An increase in temperature may first cause a decrease in virus transmissibility and then lead to a decrease in incidence rate. Also, the mechanism of the role of meteorological factors in the process of transmissibility to incidence rate needs to be further explored.

Transmissibility of hand, foot, and mouth disease in 97 counties of China

Hand, foot, and mouth disease (HFMD) is a serious disease burden in the Asia–Pacific region, including China. This study calculated the transmissibility of HFMD at county levels in Jiangsu Province, China, analyzed the differences of transmissibility and explored the possible influencing factors of its transmissibility. We built a mathematical model for seasonal characteristics of HFMD, estimated the effective reproduction number (R_eff), and compared the incidence rate and transmissibility in different counties using non-parametric tests, rapid cluster analysis and rank-sum ratio in 97 counties in Jiangsu Province from 2015 to 2020. The average daily incidence rate was between 0 and 4 per 100,000 people in Jiangsu Province from 2015–2020. The Quartile of R_eff in Jiangsu Province from 2015 to 2020 was 1.54 (0.49, 2.50). Rugao District and Jianhu District had the highest transmissibility according to the rank-sum ratio. R_eff generally decreased in 2017 and increased in 2018 in most counties, and the median level of R_eff was the lowest in 2017 (P < 0.05). The transmissibility was different in 97 counties in Jiangsu Province. The reasons for the differences may be related to the climate, demographic characteristics, virus subtypes, vaccination, hygiene and other infectious diseases.

Control measures during the COVID-19 outbreak reduced the transmission of hand, foot, and mouth disease

Control measures during the coronavirus disease 2019 (COVID-19) outbreak may have limited the spread of infectious diseases. This study aimed to analyze the impact of COVID-19 on the spread of hand, foot, and mouth disease (HFMD) in China. A mathematical model was established to fit the reported data of HFMD in six selected cities in mainland China from 2015 to 2020. The absolute difference (AD) and relative difference (RD) between the reported incidence in 2020, and simulated maximum, minimum, or median incidence of HFMD in 2015–2019 were calculated. The incidence and R_eff of HFMD have decreased in six selected cities since the outbreak of COVID-19, and in the second half of 2020, the incidence and R_eff of HFMD have rebounded. The results show that the total attack rate (TAR) in 2020 was lower than the maximum, minimum, and median TAR fitted in previous years in six selected cities (except Changsha City). For the maximum, median, minimum fitted TAR, the range of RD (%) is 42·20–99·20%, 36·35–98·41% 48·35–96·23% (except Changsha City) respectively. The preventive and control measures of COVID-19 have significantly contributed to the containment of HFMD transmission.

The epidemiological characteristics and effectiveness of countermeasures to contain coronavirus disease 2019 in Ningbo City, Zhejiang Province, China

A novel coronavirus (SARS-CoV-2) has spread worldwide and led to high disease burden around the world. This study aimed to explore the key parameters of SARS-CoV-2 infection and to assess the effectiveness of interventions to control the coronavirus disease 2019 (COVID-19). A susceptible-exposed-infectious-asymptomatic-recovered (SEIAR) model was developed for the assessment. The information of each confirmed case and asymptomatic infection was collected from Ningbo Center for Disease Control and Prevention (CDC) to calculate the key parameters of the model in Ningbo City, China. A total of 157 confirmed COVID-19 cases (including 51 imported cases and 106 secondary cases) and 30 asymptomatic infections were reported in Ningbo City. The proportion of asymptomatic infections had an increasing trend. The proportion of elder people in the asymptomatic infections was lower than younger people, and the difference was statistically significant (Fisher's Exact Test, P = 0.034). There were 22 clusters associated with 167 SARS-CoV-2 infections, among which 29 cases were asymptomatic infections, accounting for 17.37%. We found that the secondary attack rate (SAR) of asymptomatic infections was almost the same as that of symptomatic cases, and no statistical significance was observed (χ2 = 0.052, P = 0.819) by Kruskal-Wallis test. The effective reproduction number (Reff) was 1.43, which revealed that the transmissibility of SARS-CoV-2 was moderate. If the interventions had not been strengthened, the duration of the outbreak would have lasted about 16 months with a simulated attack rate of 44.15%. The total attack rate (TAR) and duration of the outbreak would increase along with the increasing delay of intervention. SARS-CoV-2 had moderate transmissibility in Ningbo City, China. The proportion of asymptomatic infections had an increase trend. Asymptomatic infections had the same transmissibility as symptomatic infections. The integrated interventions were implemented at different stages during the outbreak, which turned out to be exceedingly effective in China.

Effectiveness of potential antiviral treatments in COVID-19 transmission control: a modelling study

BACKGROUND

Novel coronavirus disease 2019 (COVID-19) causes an immense disease burden. Although public health countermeasures effectively controlled the epidemic in China, non-pharmaceutical interventions can neither be maintained indefinitely nor conveniently implemented globally. Vaccination is mainly used to prevent COVID-19, and most current antiviral treatment evaluations focus on clinical efficacy. Therefore, we conducted population-based simulations to assess antiviral treatment effectiveness among different age groups based on its clinical efficacy.

METHODS

We collected COVID-19 data of Wuhan City from published literature and established a database (from 2 December 2019 to 16 March 2020). We developed an age-specific model to evaluate the effectiveness of antiviral treatment in patients with COVID-19. Efficacy was divided into three types: (1) viral activity reduction, reflected as transmission rate decrease [reduction was set as v (0-0.8) to simulate hypothetical antiviral treatments]; (2) reduction in the duration time from symptom onset to patient recovery/removal, reflected as a 1/γ decrease (reduction was set as 1-3 days to simulate hypothetical or real-life antiviral treatments, and the time of asymptomatic was reduced by the same proportion); (3) fatality rate reduction in severely ill patients (fc) [reduction (z) was set as 0.3 to simulate real-life antiviral treatments]. The population was divided into four age groups (groups 1, 2, 3 and 4), which included those aged ≤ 14; 15-44; 45-64; and ≥ 65 years, respectively. Evaluation indices were based on outbreak duration, cumulative number of cases, total attack rate (TAR), peak date, number of peak cases, and case fatality rate (f).

RESULTS

Comparing the simulation results of combination and single medication therapy s, all four age groups showed better results with combination medication. When 1/γ = 2 and v = 0.4, age group 2 had the highest TAR reduction rate (98.48%, 56.01-0.85%). When 1/γ = 2, z = 0.3, and v = 0.1, age group 1 had the highest reduction rate of f (83.08%, 0.71-0.12%).

CONCLUSIONS

Antiviral treatments are more effective in COVID-19 transmission control than in mortality reduction. Overall, antiviral treatments were more effective in younger age groups, while older age groups showed higher COVID-19 prevalence and mortality. Therefore, physicians should pay more attention to prevention of viral spread and patients deaths when providing antiviral treatments to patients of older age groups.

Spatial-temporal mapping and risk factors for hand foot and mouth disease in northwestern inland China

Background

Hand foot and mouth disease (HFMD) is becoming one of the common human infectious diseases in China. Previous studies have described HFMD in tropical or coastal areas of Asia-Pacific countries. However, limited studies have thoroughly studied the epidemiology and potential risk factors for HFMD in inland areas with complex environmental conditions.

Methodology/Principal findings

Using the data from 2009 to 2018 on reported cases of Xinjiang Uighur Autonomous Region, we characterized the epidemic features of HFMD. Panel negative binomial model was used to identify climate, geographical and demographic determinants for HFMD incidence. A total of 70856 HFMD cases (average annual incidence: 305 per million persons) were reported in Xinjiang during the 10-year study period, of which 10393 (14.7%) were laboratory-confirmed and 98 (0.1%) were severe. HFMD peaked in summer every year during the study period, and incidence in 2012, 2015, 2016 and 2018 had minor peaks in autumn. After adjusting the school or holiday month, multiple factors were found to affect HFMD epidemiology: urban area being major land cover type (incidence risk ratio, IRR 2.08; 95% CI 1.50, 2.89), higher gross domestic product per capita (IRR 1.14; 95% CI 1.11, 1.16), rise in monthly average temperature (IRR 1.65; 95% CI 1.61, 1.69) and monthly accumulative precipitation (IRR 1.20; 95% CI 1.16, 1.24) predicted increase in the incidence of HFMD; farmland being major land cover type (IRR 0.72; 95% CI 0.64, 0.81), an increase of percentage of the minority (IRR 0.91; 95% CI 0.89, 0.93) and population density (IRR 0.98; 95% CI 0.98, 0.99) were related to a decrease in the incidence of HFMD.

Conclusions/Significance

In conclusion, the epidemic status of HFMD in Xinjiang is characterized by low morbidity and fatality. Multiple factors have significant influences on the occurrence and transmission of HFMD in Xinjiang.

Hand foot and mouth disease (HFMD) is one of the common human infectious disease threating Asia-Pacific countries. To explore the epidemiology and environmental risk factors for HFMD in inland China, we utilized 10-year HFMD surveillance data in Xinjiang Uighur Autonomous Region and combined multiple spatial-temporal statistical analyses. We identified spatial-temporal clusters of HFMD incidence and found that multiple factors could affect HFMD incidence: urban area being major land cover type, higher gross domestic product per capita, rise in monthly average temperature and monthly accumulative precipitation predicted increase in the incidence of HFMD; farmland being major land cover type, an increase of percentage of the minority and population density were related to a decrease in the incidence of HFMD. Our findings facilitate the understanding of HFMD epidemiology and risk factors in different geographic regions, which are crucial for conducting prevention and control strategies of HFMD.

A five-compartment model of age-specific transmissibility of SARS-CoV-2

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.

Interaction analysis on transmissibility of main pathogens of hand, foot, and mouth disease: A modeling study (a STROBE-compliant article)

Hand, foot, and mouth disease (HFMD) has spread widely and led to high disease burden in many countries. In this study, we aimed to analyze the interaction of the main pathogens of HFMD using a mathematical model.A dataset on reported HFMD cases was collected from April, 2009 to December, 2017 in Changsha City. A long-term etiological surveillance was conducted focusing on the pathogens of the disease including enterovirus A71 (EV71), coxsachievirus A16 (CA16), and other enteroviruses. A susceptible-infectious-recovered model was adopted to calculate the reproduction number during the ascending period of reported cases (defined as Rasc) and the descending period (defined as Rdes).About 214,178 HFMD cases (including clinically diagnosed cases and confirmed cases) were reported in Changsha City, among which 31 were death cases with a fatality of 0.01%. The number of reported HFMD cases increased yearly with a Linear model of "f(t) = 18542.68 + 1628.91t" where f(t) and t referred to number of reported cases and sequence of year, respectively. The fatality of the disease decreased yearly with a linear model of "f(t) = - 0.012 + 0.083/t". About 5319 stool or anal swab specimens were collected from the reported cases. Among them, 1201 were tested EV71 positive, 836 were CA16 positive, and 1680 were other enteroviruses positive. Rasc and Rdes of HFMD was 1.34 (95% confidence interval [CI]: 1.28-1.40) and 0.73 (95% CI: 0.69-0.76), respectively. EV71 and CA16 interacted with each other, and the interaction between EV71 and other enteroviruses and the interaction between CA16 and other enteroviruses were both directional. However, during the reported cases decreasing period, interactions only occurred between EV71 and other enteroviruses and between CA16 and other enteroviruses. These interactions all decreased Rasc but increased Rdes of affected pathogens.The interactions of the pathogens exist in Changsha City. The effective reproduction number of the affected pathogen is adjusted and verges to 1 by the interaction.