What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization

Yang, Liu; Gu, Zhonglei; Shang, Xianwen; Shi, Benyun; Zhou, Xiao-Nong; Shi, Yong; Liu, Jiming

doi:10.1016/j.eclinm.2020.100354

Cited by 144 publications

(128 citation statements)

References 16 publications

(24 reference statements)

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“…Finally, our subgroup analysis on different age groups revealed that, in the study period, the overall distribution of all-cause admissions did not change from previous years. However, focusing on distribution of infectious disease admissions, we observed a 227.6% increase in adults (N2019 = 199 vs. N2020 = 652), a 174.5% increase in elderly (N2019 = 153 vs. N2020 = 420), and a 111.1% increase in oldest old (N2019 = 54 vs. N2020 = 114); thus, suggesting a more severe impact of COVID-19 on the admission of patients belonging to the adult group, as observed by other authors [ 29 , 30 , 31 ].…”

Section: Discussionsupporting

confidence: 71%

Non-COVID Diseases during the Pandemic: Where Have All Other Emergencies Gone?

et al. 2020

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Background and objectives: the emergency department (ED) is frequently identified by patients as a possible solution for all healthcare problems, leading to a high rate of misuse of the ED, possibly causing overcrowding. The coronavirus disease 2019 (COVID-19) pandemic started in China; it then spread throughout Italy, with the first cases confirmed in Lombardy, Italy, in February 2020. This has totally changed the type of patients referred to EDs. The aim of this study was to analyze the reduction of ED admissions at a Second level urban teaching (Fondazione Policlinico Universitario Agostino Gemelli IRCCS) during the COVID-19 pandemic. Materials and Methods: in this retrospective observational cross-sectional study, we reviewed and compared clinical records of all the patients consecutively admitted to our ED over a 40-day period (21 February –31 March) in the last three years (2018–2019–2020). Mean age, sex, triage urgency level, day/night admission, main presentation symptom, and final diagnosis, according to different medical specialties, hospitalization, and discharge rate, were analyzed. Results: we analyzed 16,281 patient clinical records. The overall reduction in ED admissions in 2020 was 37.6% compared to 2019. In 2020, we observed an increase in triage urgency levels for ED admissions (the main presentation symptom was a fever). We noticed a significant drop in admissions for cardio-thoracic, gastroenterological, urological, otolaryngologic/ophthalmologic, and traumatological diseases. Acute neurological conditions registered only a slight, but significant, reduction. Oncology admissions were stable. Admissions for infectious diseases were 30% in 2020, compared to 5% and 6% in 2018 and 2019, respectively. In 2020, the hospitalization rate increased to 42.9% compared to 27.7%, and 26.4% in previous years. Conclusions: the drastic reduction of ED admissions during the pandemic may be associated with fear of the virus, suggesting that patients with serious illnesses did not go to the emergency room. Moreover, there was possible misuse of the ED in the previous year. In particular, worrisome data emerged regarding a drop in cardiology and neurology admissions. Those patients postponed medical attention, possibly with fatal consequences, just for fear of exposure to COVID-19, leading to unnecessary morbidity and mortality.

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Section: Discussionsupporting

confidence: 71%

Non-COVID Diseases during the Pandemic: Where Have All Other Emergencies Gone?

et al. 2020

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“…Epidemiological studies on SARS-CoV-2 have emphasized the importance of heterogeneity in transmission and the need for measuring transmission events and variations at the individual levels [7][8][9][10]. These studies have emphasized the importance of heterogeneity information in addition to estimating basic reproduction number.…”

Section: Discussionmentioning

confidence: 99%

“…Mathematical modelling methods are mostly adopted for assessing population-level heterogeneity of SARS-CoV-2 infections. Modelling studies have estimated the heterogeneity in terms of over-dispersion levels and proportional variations in transmission based on simulations and assumptions [7][8][9][10]. In this background, attempts to measure individual patient-level heterogeneity in SARS-CoV-2 context using real world data could be of direct public health relevance.…”

Section: Introductionmentioning

confidence: 99%

Social network analysis methods for exploring SARS-CoV-2 contact tracing data

Nagarajan

Muniyandi

Palani

et al. 2020

BMC Med Res Methodol

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Background Contact tracing data of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic is used to estimate basic epidemiological parameters. Contact tracing data could also be potentially used for assessing the heterogeneity of transmission at the individual patient level. Characterization of individuals based on different levels of infectiousness could better inform the contact tracing interventions at field levels. Methods Standard social network analysis methods used for exploring infectious disease transmission dynamics was employed to analyze contact tracing data of 1959 diagnosed SARS-CoV-2 patients from a large state of India. Relational network data set with diagnosed patients as “nodes” and their epidemiological contact as “edges” was created. Directed network perspective was utilized in which directionality of infection emanated from a “source patient” towards a “target patient”. Network measures of “ degree centrality” and “betweenness centrality” were calculated to identify influential patients in the transmission of infection. Components analysis was conducted to identify patients connected as sub- groups. Descriptive statistics was used to summarise network measures and percentile ranks were used to categorize influencers. Results Out-degree centrality measures identified that of the total 1959 patients, 11.27% (221) patients have acted as a source of infection to 40.19% (787) other patients. Among these source patients, 0.65% (12) patients had a higher out-degree centrality (> = 10) and have collectively infected 37.61% (296 of 787), secondary patients. Betweenness centrality measures highlighted that 7.50% (93) patients had a non-zero betweenness (range 0.5 to 135) and thus have bridged the transmission between other patients. Network component analysis identified nineteen connected components comprising of influential patient’s which have overall accounted for 26.95% of total patients (1959) and 68.74% of epidemiological contacts in the network. Conclusions Social network analysis method for SARS-CoV-2 contact tracing data would be of use in measuring individual patient level variations in disease transmission. The network metrics identified individual patients and patient components who have disproportionately contributed to transmission. The network measures and graphical tools could complement the existing contact tracing indicators and could help improve the contact tracing activities.

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“…To achieve this goal, the first priority is to assess the epidemiological characteristics of the COVID-19 pandemic in each country. Because populations in different countries have different contact patterns, the epidemiological parameters (e.g., the basic reproduction number R 0 ) of COVID-19 may also vary in different countries [24,25]. Even in the same country, the epidemiological parameters measured with different data at different stages may also be different.…”

Section: Discussionmentioning

confidence: 99%

Accessing the Syndemic of COVID-19 and Malaria Intervention in Africa

Shi

Zheng²,

Shang³

et al. 2020

Preprint

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Background: The COVID-19 pandemic has caused substantial disruptions to health services in the low and middle-income countries with a high burden of other diseases, such as malaria in sub-Saharan Africa. As the COVID-19 pandemic spread to Africa, there is an urgent need to assess the impact of COVID-19 pandemic on malaria transmission potential in malaria-endemic countries in Africa.Methods: We present a data-driven method to quantify the extent to which the COVID-19 pandemic, as well as various non-pharmaceutical interventions (NPIs), could lead to the change of malaria transmission potential in 2020. First, we adopt a particle Markov Chain Monte Carlo method to estimate epidemiological parameters in each country by fitting the time series of the cumulative number of COVID-19 cases. Then, we simulate the epidemic dynamics of COVID-19 under two groups of NPIs: (i) contact restriction and social distancing, and (ii) early identification and isolation of cases. Based on the simulated epidemic curves, we quantify the impact of COVID-19 epidemic and NPIs on the distribution of insecticide-treated nets (ITNs). Finally, by treating the total number of ITNs available in each country in 2020, we evaluate the negative effects of COVID-19 pandemic on malaria transmission potential based on the notion of vectorial capacity.Results: We conduct case studies in four malaria-endemic countries, Ethiopia, Nigeria, Tanzania, and Zambia, in Africa. The epidemiological parameters (i.e., the basic reproduction number R0 and the duration of infection DI ) of COVID-19 in each country are estimated as follows: Ethiopia (R0 = 1:57, DI = 5:32), Nigeria (R0 = 2:18, DI = 6:58), Tanzania (R0 = 2:47, DI = 6:01), and Zambia (R0 = 2:12, DI = 6:96). Based on the estimated epidemiological parameters, the epidemic curves simulated under various NPIs indicated that the earlier the interventions are implemented, the better the epidemic is controlled. Moreover, the effect of combined NPIs is better than contact restriction and social distancing only. By treating the total number of ITNs available in each country in 2020 as a baseline, our results show that even with stringent NPIs, malaria transmission potential will remain higher than expected in the second half of 2020.Conclusions: By quantifying the impact of various NPI response to the COVID-19 pandemic on malaria transmission potential, this study provides a way to jointly address the syndemic between COVID-19 and malaria in malaria-endemic countries in Africa. The results suggest that the early intervention of COVID-19 can effectively reduce the scale of the epidemic and mitigate its impact on malaria transmission potential.

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What are the underlying transmission patterns of COVID-19 outbreak? An age-specific social contact characterization

Cited by 144 publications

References 16 publications

Non-COVID Diseases during the Pandemic: Where Have All Other Emergencies Gone?

Non-COVID Diseases during the Pandemic: Where Have All Other Emergencies Gone?

Social network analysis methods for exploring SARS-CoV-2 contact tracing data

Accessing the Syndemic of COVID-19 and Malaria Intervention in Africa

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