Estimated Incubation Period and Serial Interval for Human-to-Human Influenza A(H7N9) Virus Transmission

Primary care-based health surveillance actions in response to the COVID-19 pandemic: contributions to the debate

We conducted an integrated literature review aimed at reflecting on the challenges related to primary care-based health surveillance actions in response to the COVID-19 pandemic in selected countries. The study included countries with different PHC models that adopted surveillance as an approach to control the transmission of COVID-19. We performed a search in October 2020 for relevant literature and norms and guidelines related to the organization of primary health care (PHC) in response to the pandemic on official government websites and the databases Web of Science and Science Direct. The integrated health surveillance actions demonstrated that efforts were more focused on risks, with some countries adopting innovative and effective measures to respond to COVID-19, considering emerging needs within PHC. However, in addition to ethical controversies and operational difficulties, access to technology was a challenge in actions developed by some countries due to social inequalities.

Spatial transmission network construction of influenza-like illness using dynamic Bayesian network and vector-autoregressive moving average model

BACKGROUND

Although vaccination is one of the main countermeasures against influenza epidemic, it is highly essential to make informed prevention decisions to guarantee that limited vaccination resources are allocated to the places where they are most needed. Hence, one of the fundamental steps for decision making in influenza prevention is to characterize its spatio-temporal trend, especially on the key problem about how influenza transmits among adjacent places and how much impact the influenza of one place could have on its neighbors. To solve this problem while avoiding too much additional time-consuming work on data collection, this study proposed a new concept of spatio-temporal route as well as its estimation methods to construct the influenza transmission network.

METHODS

The influenza-like illness (ILI) data of Sichuan province in 21 cities was collected from 2010 to 2016. A joint pattern based on the dynamic Bayesian network (DBN) model and the vector autoregressive moving average (VARMA) model was utilized to estimate the spatio-temporal routes, which were applied to the two stages of learning process respectively, namely structure learning and parameter learning. In structure learning, the first-order conditional dependencies approximation algorithm was used to generate the DBN, which could visualize the spatio-temporal routes of influenza among adjacent cities and infer which cities have impacts on others in influenza transmission. In parameter learning, the VARMA model was adopted to estimate the strength of these impacts. Finally, all the estimated spatio-temporal routes were put together to form the final influenza transmission network.

RESULTS

The results showed that the period of influenza transmission cycle was longer in Western Sichuan and Chengdu Plain than that in Northeastern Sichuan, and there would be potential spatio-temporal routes of influenza from bordering provinces or municipalities into Sichuan province. Furthermore, this study also pointed out several estimated spatio-temporal routes with relatively high strength of associations, which could serve as clues of hot spot areas detection for influenza surveillance.

CONCLUSIONS

This study proposed a new framework for exploring the potentially stable spatio-temporal routes between different places and measuring specific the sizes of transmission effects. It could help making timely and reliable prediction of the spatio-temporal trend of infectious diseases, and further determining the possible key areas of the next epidemic by considering their neighbors' incidence and the transmission relationships.

Estimating the serial interval of the novel coronavirus disease (COVID-19) based on the public surveillance data in Shenzhen, China, from 19 January to 22 February 2020

The novel coronavirus disease (COVID-19) poses a serious threat to global public health and economics. Serial interval (SI), time between the onset of symptoms of a primary case and a secondary case, is a key epidemiological parameter. We estimated SI of COVID-19 in Shenzhen, China based on 27 records of transmission chains. We adopted three parametric models: Weibull, lognormal and gamma distributions, and an interval-censored likelihood framework. The three models were compared using the corrected Akaike information criterion (AICc). We also fitted the epidemic curve of COVID-19 to the logistic growth model to estimate the reproduction number. Using a Weibull distribution, we estimated the mean SI to be 5.9 days (95% CI: 3.9-9.6) with a standard deviation (SD) of 4.8 days (95% CI: 3.1-10.1). Using a logistic growth model, we estimated the basic reproduction number in Shenzhen to be 2.6 (95% CI: 2.4-2.8). The SI of COVID-19 is relatively shorter than that of SARS and MERS, the other two betacoronavirus diseases, which suggests the iteration of the transmission may be rapid. Thus, it is crucial to isolate close contacts promptly to effectively control the spread of COVID-19.