A Dynamic Compartmental Model to Explore the Optimal Strategy of Varicella Vaccination: An Epidemiological Study in Jiangsu Province, China.

Varicella (chickenpox) is highly contagious among children and frequently breaks out in schools. In this study, we developed a dynamic compartment model to explore the optimal schedule for varicella vaccination in Jiangsu Province, China. A susceptible-infected-recovered (SIR) model was proposed to simulate the transmission of varicella in different age groups. The basic reproduction number was computed by the kinetic model, and the impact of three prevention factors was assessed through the global sensitivity analysis. Finally, the effect of various vaccination scenarios was qualitatively evaluated by numerical simulation. The estimated basic reproduction number was 1.831 ± 0.078, and the greatest contributor was the 5-10 year-old group (0.747 ± 0.042, 40.80%). Sensitivity analysis indicated that there was a strong negative correlation between the second dose vaccination coverage rate and basic reproduction number. In addition, we qualitatively found that the incidence would significantly decrease as the second dose vaccine coverage expands. The results suggest that two-dose varicella vaccination should be mandatory, and the optimal age of second dose vaccination is the 5-10 year-old group. Optimal vaccination time, wide vaccine coverage along with other measures, could enhance the effectiveness of prevention and control of varicella in China.

Massive migration promotes the early spread of COVID-19 in China: a study based on a scale-free network.

BACKGROUND: The coronavirus disease 2019 (COVID-19) epidemic met coincidentally with massive migration before Lunar New Year in China in early 2020. This study is to investigate the relationship between the massive migration and the coronavirus disease 2019 (COVID-19) epidemic in China. METHODS: The epidemic data between January 25th and February 15th and migration data between Jan 1st and Jan 24th were collected from the official websites. Using the R package WGCNA, we established a scale-free network of the selected cities. Correlation analysis was applied to describe the correlation between the Spring Migration and COVID-19 epidemic. RESULTS: The epidemic seriousness in Hubei (except the city of Wuhan) was closely correlated with the migration from Wuhan between January 10 and January 24, 2020. The epidemic seriousness in the other provinces, municipalities and autonomous regions was largely affected by the immigration from Wuhan. By establishing a scale-free network of the regions, we divided the regions into two modules. The regions in the brown module consisted of three municipalities, nine provincial capitals and other 12 cities. The COVID-19 epidemics in these regions were more likely to be aggravated by migration. CONCLUSIONS: The migration from Wuhan could partly explain the epidemic seriousness in Hubei Province and other regions. The scale-free network we have established can better evaluate the epidemic. Three municipalities (Beijing, Shanghai and Tianjin), eight provincial capitals (including Nanjing, Changsha et al.) and 12 other cities (including Qingdao, Zhongshan, Shenzhen et al.) were hub cities in the spread of COVID-19 in China.

Linking key intervention timings to rapid declining effective reproduction number to quantify lessons against COVID-19.

Coronavirus disease 2019 (COVID-19) is currently under a global pandemic trend. The efficiency of containment measures and epidemic tendency of typical countries should be assessed. In this study, the efficiency of prevention and control measures in China, Italy, Iran, South Korea, and Japan was assessed, and the COVID-19 epidemic tendency among these countries was compared. Results showed that the effective reproduction number(Re) in Wuhan, China increased almost exponentially, reaching a maximum of 3.98 before a lockdown and rapidly decreased to below 1 due to containment and mitigation strategies of the Chinese government. The Re in Italy declined at a slower pace than that in China after the implementation of prevention and control measures. The Re in Iran showed a certain decline after the establishment of a national epidemic control command, and an evident stationary phase occurred because the best window period for the prevention and control of the epidemic was missed. The epidemic in Japan and South Korea reoccurred several times with the Re fluctuating greatly. The epidemic has hardly rebounded in China due to the implementation of prevention and control strategies and the effective enforcement of policies. Other countries suffering from the epidemic could learn from the Chinese experience in containing COVID-19.

Spatial statistical analysis of Coronavirus Disease 2019 (Covid-19) in China.

The cluster of pneumonia cases linked to coronavirus disease 2019 (Covid-19), first reported in China in late December 2019 raised global concern, particularly as the cumulative number of cases reported between 10 January and 5 March 2020 reached 80,711. In order to better understand the spread of this new virus, we characterized the spatial patterns of Covid-19 cumulative cases using ArcGIS v.10.4.1 based on spatial autocorrelation and cluster analysis using Global Moran's I (Moran, 1950), Local Moran's I and Getis-Ord General G (Ord and Getis, 2001). Up to 5 March 2020, Hubei Province, the origin of the Covid-19 epidemic, had reported 67,592 Covid-19 cases, while the confirmed cases in the surrounding provinces Guangdong, Henan, Zhejiang and Hunan were 1351, 1272, 1215 and 1018, respectively. The top five regions with respect to incidence were the following provinces: Hubei (11.423/10,000), Zhejiang (0.212/10,000), Jiangxi (0.201/10,000), Beijing (0.196/10,000) and Chongqing (0.186/10,000). Global Moran's I analysis results showed that the incidence of Covid-19 is not negatively correlated in space (p=0.407413>0.05) and the High-Low cluster analysis demonstrated that there were no high-value incidence clusters (p=0.076098>0.05), while Local Moran's I analysis indicated that Hubei is the only province with High-Low aggregation (p<0.0001).