Characterizing superspreading potential of infectious disease: Decomposition of individual transmissibility.

In the context of infectious disease transmission, high heterogeneity in individual infectiousness indicates that a few index cases can generate large numbers of secondary cases, a phenomenon commonly known as superspreading. The potential of disease superspreading can be characterized by describing the distribution of secondary cases (of each seed case) as a negative binomial (NB) distribution with the dispersion parameter, k. Based on the feature of NB distribution, there must be a proportion of individuals with individual reproduction number of almost 0, which appears restricted and unrealistic. To overcome this limitation, we generalized the compound structure of a Poisson rate and included an additional parameter, and divided the reproduction number into independent and additive fixed and variable components. Then, the secondary cases followed a Delaporte distribution. We demonstrated that the Delaporte distribution was important for understanding the characteristics of disease transmission, which generated new insights distinct from the NB model. By using real-world dataset, the Delaporte distribution provides improvements in describing the distributions of COVID-19 and SARS cases compared to the NB distribution. The model selection yielded increasing statistical power with larger sample sizes as well as conservative type I error in detecting the improvement in fitting with the likelihood ratio (LR) test. Numerical simulation revealed that the control strategy-making process may benefit from monitoring the transmission characteristics under the Delaporte framework. Our findings highlighted that for the COVID-19 pandemic, population-wide interventions may control disease transmission on a general scale before recommending the high-risk-specific control strategies.

The time and risk factors of virus nucleic acid negative conversion in 104 cases of COVID-19 in Chongqing

Objective The time and the risk factors of virus nucleic acids negative conversion of coronavirus disease 2019 (COVID-19) patients were analyzed. Methods The clinical data of patients with COVID-19 admitted to Chongqing Public Health Medical Treatment Center from February 6 to March 9, 2020 were collected retrospectively. The clinical characteristics, treatment and outcome were described. The time of turning negative of nucleic acid was counted and the risk factors of turning negative of nucleic acid were analyzed. Results There were 104 confirmed cases of COVID-19, 56 males and 48 females, with an average age of (49.9 +or- 15.5) years (12-86 years old). The average interval between clinical symptoms and medical treatment was 5.5 days. From the time point of clinical symptoms, the time needed for nucleic acid to turn negative was (19.8 +or- 7.7) days, of which 32 cases (30.8%) turned negative within 14 days, 72 cases (69.2%) turned negative more than 14 days, and 14 cases (13.5%) turned negative more than 28 days. Univariate analysis showed that the time interval from clinical symptoms to medical treatment (6.7+or-4.4) days of nucleic acid conversion group after 14 days was longer than that (2.6 +or-1.8) days of conversion group within 14 days (P < 0.05);the incidence of combined with pulmonary bacterial infection (25 cases, 34.7%), infection more than three pulmonary lobes (36 cases, 50.0%), severe patients (24 cases, 33.3%) and combined use of glucocorticoids were greater than those of conversion group within 14 days (all P < 0.05). Multivariate analysis showed that there were significant differences in the time interval from symptoms to medical treatment and the proportion of three or more lobes involved in lung lesions between the two groups (P < 0.05). Conclusions The risk factors of viral nucleic acid turning negative are the timeliness of medical treatment, combined with pulmonary bacterial infection, wide range of pulmonary lesions, clinical typing and combined use of glucocorticoids, especially the timeliness of medical treatment and the range of pulmonary lesions.

Change in eating habits and physical activities before and during the COVID-19 pandemic in Hong Kong: a cross-sectional study via random telephone survey.

BACKGROUND: Hong Kong is a densely populated city with a low incidence and mortality of coronavirus disease 2019 (COVID-19). The city imposed different levels of social distancing including, the closure of sports venues and restrictions on eateries. This inevitably affects the eating behaviour and physical activities of the population. We examined the changes in eating behavior and physical activities before and during the COVID-19 pandemic, and identified sociodemographic factors associated with the behavioral changes. METHODS: This was a cross-sectional study via a random telephone survey of Chinese adults conducted in Hong Kong from May to June, 2020 - a period in which social distancing measures were being imposed. We measured the physical activity habits from four aspects and dietary consumption patterns from seven aspects before and during the pandemic based on the World Health Organization's guidelines and previous publications. RESULTS: In total, 724 participants were recruited. Individuals were found to cook more frequently at home (p < 0.001) and order take-out (p < 0.001) during the COVID-19 pandemic. While no significant change in the frequency of fast food consumption was observed, we found significant increases in the frequency of eating fruits (p < 0.001) and vegetables (p = 0.004). The frequencies of walking, moderate-intensive sports, and high-intensity sports were significantly reduced (p < 0.001). We found that healthy lifestyle behaviors during the pandemic were negatively associated with participants' economic status. CONCLUSIONS: Social distancing measures likely provided an opportunity for individuals to stay home and thus eat healthier. However, in a prolonged period of social restrictions, a lower physical activity level poses a risk to public health. Public health officials are thus advised to monitor physical health on a population-wide basis. The findings highlighted the importance of interventions tailored to individuals who have prolonged home stays - particularly for individuals in the low economic group.

The changing patterns of COVID-19 transmissibility during the social unrest in the United States: A nationwide ecological study with a before-and-after comparison.

Nationwide mass social unrest has emerged in the US since May 25 and raised broad concerns about its impacts on the local COVID-19 epidemics. We compared the COVID-19 transmissibility between May 19-May 25 and May 29-June 4 for each state of the US. We found that social unrest is likely associated with the rebound of the COVID-19 transmissibility, which might raise difficulties in the pandemic control.

Estimation of COVID-19 under-ascertainment in Kano, Nigeria during the early phase of the epidemics

Estimating the number of cases under-ascertained by inconsistencies is an essential concept in epidemiology. The aim of this study is to estimate the number of COVID-19 under- ascertained (η), and the basic reproduction number (R0) in Kano, Nigeria during the early epidemic period. We adopt a simple exponential growth model to capture the COVID-19 epidemic curve in Kano. Our findings indicate that the early epidemic growth mimics an exponential growth pattern. We find that the number of COVID-19 cases under-ascertained likely occurred during the fourth week of April 2020, and should be considered for future epidemiological investigations and mitigation plan.

Association of time to diagnosis with socioeconomic position and geographical accessibility to healthcare among symptomatic COVID-19 patients: A retrospective study in Hong Kong.

Early diagnosis is important to control COVID-19 outbreaks. This study aimed to assess how individual and area socioeconomic position and geographical accessibility to healthcare services were associated with the time to diagnosis among symptomatic COVID-19 patients in Hong Kong. Multivariable generalized linear regression was used to estimate the associations while adjusting for sociodemographic characteristics and case classification. This study found living in public rental housing and living in an area with low education were associated with longer time to diagnosis in the first wave of infections. Specifically, the risk of delayed diagnosis for public rental housing residents was mitigated by the higher density of public clinics/hospitals but was slightly increased by the higher density of private medical practitioners nearby. No such relations were found in the second wave of infections when the surveillance measures were enhanced. Given the grave impact of pandemics around the world, our findings call on taking inequalities into account when public health policies are being devised.