Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent for coronavirus 2019 (COVID-19) pneumonia. Little is known about the kinetics, tissue distribution, cross-reactivity, and neutralization antibody response in patients with COVID-19. Two groups of patients with RT-PCR–confirmed COVID-19 were enrolled in this study: 12 severely ill patients in intensive care units who needed mechanical ventilation and 11 mildly ill patients in isolation wards. Serial clinical samples were collected for laboratory detection. Results showed that most of the severely ill patients had viral shedding in a variety of tissues for 20–40 days after onset of disease (8/12, 66.7%), while the majority of mildly ill patients had viral shedding restricted to the respiratory tract and had no detectable virus RNA 10 days after onset (9/11, 81.8%). Mildly ill patients showed significantly lower IgM response compared with that of the severe group. IgG responses were detected in most patients in both the severe and mild groups at 9 days after onset, and remained at a high level throughout the study. Antibodies cross-reactive to SARS-CoV and SARS-CoV-2 were detected in patients with COVID-19 but not in patients with MERS. High levels of neutralizing antibodies were induced after about 10 days after onset in both severely and mildly ill patients which were higher in the severe group. SARS-CoV-2 pseudotype neutralization test and focus reduction neutralization test with authentic virus showed consistent results. Sera from patients with COVID-19 inhibited SARS-CoV-2 entry. Sera from convalescent patients with SARS or Middle East respiratory syndrome (MERS) did not. Anti–SARS-CoV-2 S and N IgG levels exhibited a moderate correlation with neutralization titers in patients’ plasma. This study improves our understanding of immune response in humans after SARS-CoV-2 infection.

Dengue fever is a severe public heath challenge in south China. A dengue outbreak was reported in Chaozhou city, China in 2015. Intensified interventions were implemented by the government to control the epidemic. However, it is still unknown the degree to which intensified control measures reduced the size of the epidemics, and when should such measures be initiated to reduce the risk of large dengue outbreaks developing? We selected Xiangqiao district as study setting because the majority of the indigenous cases (90.6%) in Chaozhou city were from this district. The numbers of daily indigenous dengue cases in 2015 were collected through the national infectious diseases and vectors surveillance system, and daily Breteau Index (BI) data were reported by local public health department. We used a compartmental dynamic SEIR (Susceptible, Exposed, Infected and Removed) model to assess the effectiveness of control interventions, and evaluate the control effect of intervention timing on dengue epidemic. A total of 1250 indigenous dengue cases was reported from Xiangqiao district. The results of SEIR modeling using BI as an indicator of actual control interventions showed a total of 1255 dengue cases, which is close to the reported number (n = 1250). The size and duration of the outbreak were highly sensitive to the intensity and timing of interventions. The more rigorous and earlier the control interventions implemented, the more effective it yielded. Even if the interventions were initiated several weeks after the onset of the dengue outbreak, the interventions were shown to greatly impact the prevalence and duration of dengue outbreak. This study suggests that early implementation of rigorous dengue interventions can effectively reduce the epidemic size and shorten the epidemic duration.

Background: As a mosquito-borne infectious disease, dengue fever (DF) has spread through tropical and subtropical regions worldwide in recent decades. Dengue forecasting is essential for enhancing the effectiveness of preventive measures. Current studies have been primarily conducted at national, sub-national, and city levels, while an intra-urban dengue forecasting at a fine spatial resolution still remains a challenging feat. As viruses spread rapidly because of a highly dynamic population flow, integrating spatial interactions of human movements between regions would be potentially beneficial for intra-urban dengue forecasting. Methodology: In this study, a new framework for enhancing intra-urban dengue forecasting was developed by integrating the spatial interactions between urban regions. First, a graph-embedding technique called Node2Vec was employed to learn the embeddings (in the form of an N-dimensional real-valued vector) of the regions from their population flow network. As strongly interacting regions would have more similar embeddings, the embeddings can serve as “interaction features.” Then, the interaction features were combined with those commonly used features (e.g., temperature, rainfall, and population) to enhance the supervised learning–based dengue forecasting models at a fine-grained intra-urban scale. Results: The performance of forecasting models (i.e., SVM, LASSO, and ANN) integrated with and without interaction features was tested and compared on township-level dengue forecasting in Guangzhou, the most threatened sub-tropical city in China. Results showed that models using both common and interaction features can achieve better performance than that using common features alone. Conclusions: The proposed approach for incorporating spatial interactions of human movements using graph-embedding technique is effective, which can help enhance fine-grained intra-urban dengue forecasting.

An international spread of Zika virus (ZIKV) infection has attracted global attention in 2015. The infection also affected Guangdong province, which is located in southern China. Multiple factors, including frequent communication with South America and Southeast Asia, suitable climate (sub-tropical) for the habitat of Aedes species, may increase the risk of ZIKV disease transmission in this region. An analytic hierarchy process (AHP) method was used to develop a semi-quantitative ZIKV risk assessment model. After selecting indicators, we invited experts in related professions to identify the index weight and based on that a hierarchical structure was generated. Then a series of pairwise comparisons were used to determine the relative importance of the criteria. Finally, the optimal model was established to estimate the spatial and seasonal transmission risk of ZIKV. A total of 15 factors that potentially influenced the risk of ZIKV transmission were identified. The factor that received the largest weight was epidemic of ZIKV in Guangdong province (combined weight [CW] =0.37), followed by the mosquito density (CW = 0.18) and the epidemic of DENV in Guangdong province (CW = 0.14). The distribution of 123 districts/counties’ RIs of ZIKV in Guangdong through different seasons were presented, respectively. Higher risk was observed within Pearl River Delta including Guangzhou, Shenzhen and Jiangmen, and the risk is greater in summer and autumn compared to spring and winter.

The moving epidemic method (MEM) has been well used for assessing seasonal influenza epidemics in temperate regions. This study used the MEM to establish epidemic threshold for influenza in Guangdong, a subtropical province in China. Influenza virology surveillance data from 2011/2012 to 2017/2018 seasons in Guangdong were used with the MEM to calculate the epidemic thresholds and timeously detect the 2018/2019 influenza season epidemic. The weekly positive proportion of influenza A(H1N1)pdm09, A(H3N2), B/Victoria-lineage and B/Yamagata-lineage were separately adapted to calculate the subtype-specific epidemic thresholds. The performance of MEM was evaluated using a cross-validation procedure. For the 2018/2019 influenza season, the epidemic threshold of a weekly positive proportion was 15.08%. Epidemic detection for the 2018/2019 season was 1 week in advance. Influenza A(H1N1)pdm09, B/Yamagata-lineage and B/Victoria-lineage prevailed during winter and spring and their epidemic thresholds were 5.12%, 4.53% and 4.38%, respectively. Influenza A(H3N2) was active in the summer, with an epidemic threshold of 11.99%. Using influenza virology surveillance data stratified by types of influenza virus, the MEM was effectively used in Guangdong, China. This study provided a practical way for subtropical regions to establish local influenza epidemic thresholds.

Tie Song and colleagues argue that a 14 day quarantine of inbound passengers is an effective and proportionate measure to control imported covid-19