epicontacts: Handling, visualisation and analysis of epidemiological contacts

Analysis of contact tracing data showed contribution of asymptomatic and non-severe infections to the maintenance of SARS-CoV-2 transmission in Senegal

During the COVID-19 pandemic in Senegal, contact tracing was done to identify transmission clusters, their analysis allowed to understand their dynamics and evolution. In this study, we used information from the surveillance data and phone interviews to construct, represent and analyze COVID-19 transmission clusters from March 2, 2020, to May 31, 2021. In total, 114,040 samples were tested and 2153 transmission clusters identified. A maximum of 7 generations of secondary infections were noted. Clusters had an average of 29.58 members and 7.63 infected among them; their average duration was 27.95 days. Most of the clusters (77.3%) are concentrated in Dakar, capital city of Senegal. The 29 cases identified as super-spreaders, i.e., the indexes that had the most positive contacts, showed few symptoms or were asymptomatic. Deepest transmission clusters are those with the highest percentage of asymptomatic members. The correlation between proportion of asymptomatic and degree of transmission clusters showed that asymptomatic strongly contributed to the continuity of transmission within clusters. During this pandemic, all the efforts towards epidemiological investigations, active case-contact detection, allowed to identify in a short delay growing clusters and help response teams to mitigate the spread of the disease.

Increased transmission of SARS-CoV-2 in Denmark during UEFA European championships

Denmark hosted four games during the 2020 UEFA European championships (EC2020). After declining positive SARS-CoV-2 test rates in Denmark, a rise occurred during and after the tournament, concomitant with the replacement of the dominant Alpha lineage (B.1.1.7) by the Delta lineage (B.1.617.2), increasing vaccination rates and cessation of several restrictions. A cohort study including 33 227 cases was conducted from 30 May to 25 July 2021, 14 days before and after the EC2020. Included was a nested cohort with event information from big-screen events and matches at the Danish national stadium, Parken (DNSP) in Copenhagen, held from 12 June to 28 June 2021. Information from whole-genome sequencing, contact tracing and Danish registries was collected. Case–case connections were used to establish transmission trees. Cases infected on match days were compared to cases not infected on match days as a reference. The crude incidence rate ratio (IRR) of transmissions was 1.55, corresponding to 584 (1.76%) cases attributable to EC2020 celebrations. The IRR adjusted for covariates was lower (IRR 1.41) but still significant, and also pointed to a reduced number of transmissions from fully vaccinated cases (IRR 0.59). These data support the hypothesis that the EC2020 celebrations contributed to the rise of cases in Denmark in the early summer of 2021.

Tracing COVID-19 Source of Infection Among Health Personnel in a Pediatric Hospital

Health personnel (HP) have been universally recognized as especially susceptible to COVID-19. In Mexico, our home country, HP has one of the highest death rates from the disease. From the beginning of the SARS-CoV-2 pandemic, an office for initial attention for HP and a call center were established at a COVID-19 national reference pediatric hospital, aimed at early detection of COVID-19 cases and stopping local transmission. The detection and call center implementation and operation, and tracing methodology are described here. A total of 1,042 HP were evaluated, with 221 positive cases identified (7.7% of all HP currently working and 26% of the HP tested). Community contagion was most prevalent (46%), followed by other HP (27%), household (14%), and hospitalized patients (13%). Clusters and contact network analysis are discussed. This is one of the first reports that address the details of the implementation process of contact tracing in a pediatric hospital from the perspective of a hybrid hospital with COVID-19 and non-COVID-19 areas.

Presentation of a developed sub-epidemic model for estimation of the COVID-19 pandemic and assessment of travel-related risks in Iran

The COVID-19 pandemic is one of the contagious diseases involving all the world in 2019-2020. Also, all people are concerned about the future of this catastrophe and how the continuous outbreak can be prevented. Some countries are not successful in controlling the outbreak; therefore, the incidence is observed in several peaks. In this paper, firstly single-peak SIR models are used for historical data. Regarding the SIR model, the termination time of the outbreak should have been in early June 2020. However, several peaks invalidate the results of single-peak models. Therefore, we should present a model to support pandemics with several extrema. In this paper, we presented the generalized logistic growth model (GLM) to estimate sub-epidemic waves of the COVID-19 outbreak in Iran. Therefore, the presented model simulated scenarios of two, three, and four waves in the observed incidence. In the second part of the paper, we assessed travel-related risk in inter-provincial travels in Iran. Moreover, the results of travel-related risk show that typical travel between Tehran and other sites exposed Isfahan, Gilan, Mazandaran, and West Azerbaijan in the higher risk of infection greater than 100 people per day. Therefore, controlling this movement can prevent great numbers of infection, remarkably.

Chainchecker: An application to visualise and explore transmission chains for Ebola virus disease

2020 saw the continuation of the second largest outbreak of Ebola virus disease (EVD) in history. Determining epidemiological links between cases is a key part of outbreak control. However, due to the large quantity of data and subsequent data entry errors, inconsistencies in potential epidemiological links are difficult to identify. We present chainchecker, an online and offline shiny application which visualises, curates and verifies transmission chain data. The application includes the calculation of exposure windows for individual cases of EVD based on user defined incubation periods and user specified symptom profiles. It has an upload function for viral hemorrhagic fever data and utility for additional entries. This data may then be visualised as a transmission tree with inconsistent links highlighted. Finally, there is utility for cluster analysis and the ability to highlight nosocomial transmission. chainchecker is a R shiny application which has an offline version for use with VHF (viral hemorrhagic fever) databases or linelists. The software is available at https://shiny.dide.imperial.ac.uk/chainchecker which is a web-based application that links to the desktop application available for download and the github repository, https://github.com/imperialebola2018/chainchecker.

COVID-19 epidemic monitoring after non-pharmaceutical interventions: The use of time-varying reproduction number in a country with a large migrant population

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R_t can be used to closely monitor the non-pharmaceutical interventions (NPI) of the COVID-19 epidemic.

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Activity of the epidemic in Oman is examined.

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Other factors, like the return of overseas students, have increased the epidemic activity.

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Responses to NPI are different between migrants and natives.

Background

COVID-19’s emergence carries with it many uncertainties and challenges, including strategies to manage the epidemic. Oman has implemented non-pharmaceutical interventions (NPIs) to mitigate the impact of COVID-19. However, responses to NPIs may be different across different populations within a country with a large number of migrants, such as Oman. This study investigated the different responses to NPIs, and assessed the use of the time-varying reproduction number (R_t) to monitor them.

Methods

Polymerase chain reaction (PCR) laboratory-confirmed COVID-19 data for Oman, from February 24 to June 3, 2020, were used alongside demographic and epidemiological information. Data were arranged into pairs of infector–infectee, and two main libraries of R software were used to estimate reproductive number (R_t). R_t was calculated for both Omanis and non-Omanis.

Findings

A total of 13,538 cases were included, 44.9% of which were Omanis. Among all these cases we identified 2769 infector–infectee pairs for calculating R_t. There was a sharp drop in R_t from 3.7 (95% confidence interval [CI] 2.8–4.6) in mid-March to 1.4 (95% CI 1.2–1.7) in late March in response to NPIs. R_t then decreased further to 1.2 (95% CI 1.1–1.3) in late April after which it rose, corresponding to the easing of NPIs. Comparing the two groups, the response to major public health controls was more evident in Omanis in reducing R_t to 1.09 (95% CI 0.84–1.3) by the end of March.

Interpretation

Use of real-time estimation of R_t allowed us to follow the effects of NPIs. The migrant population responded differently than the Omani population.

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'

On 21 February 2020, a resident of the municipality of Vo', a small town near Padua (Italy), died of pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection¹. This was the first coronavirus disease 19 (COVID-19)-related death detected in Italy since the detection of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province². In response, the regional authorities imposed the lockdown of the whole municipality for 14 days³. Here we collected information on the demography, clinical presentation, hospitalization, contact network and the presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. From the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI): 2.1-3.3%). From the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI: 0.8-1.8%). Notably, 42.5% (95% CI: 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (that is, did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI: 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (P = 0.62 and 0.74 for E and RdRp genes, respectively, exact Wilcoxon-Mann-Whitney test). This study sheds light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides insights into its transmission dynamics and the efficacy of the implemented control measures.

Suppression of a SARS-CoV-2 outbreak in the Italian municipality of Vo'

On 21 February 2020, a resident of the municipality of Vo', a small town near Padua (Italy), died of pneumonia due to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection¹. This was the first coronavirus disease 19 (COVID-19)-related death detected in Italy since the detection of SARS-CoV-2 in the Chinese city of Wuhan, Hubei province². In response, the regional authorities imposed the lockdown of the whole municipality for 14 days³. Here we collected information on the demography, clinical presentation, hospitalization, contact network and the presence of SARS-CoV-2 infection in nasopharyngeal swabs for 85.9% and 71.5% of the population of Vo' at two consecutive time points. From the first survey, which was conducted around the time the town lockdown started, we found a prevalence of infection of 2.6% (95% confidence interval (CI): 2.1-3.3%). From the second survey, which was conducted at the end of the lockdown, we found a prevalence of 1.2% (95% CI: 0.8-1.8%). Notably, 42.5% (95% CI: 31.5-54.6%) of the confirmed SARS-CoV-2 infections detected across the two surveys were asymptomatic (that is, did not have symptoms at the time of swab testing and did not develop symptoms afterwards). The mean serial interval was 7.2 days (95% CI: 5.9-9.6). We found no statistically significant difference in the viral load of symptomatic versus asymptomatic infections (P = 0.62 and 0.74 for E and RdRp genes, respectively, exact Wilcoxon-Mann-Whitney test). This study sheds light on the frequency of asymptomatic SARS-CoV-2 infection, their infectivity (as measured by the viral load) and provides insights into its transmission dynamics and the efficacy of the implemented control measures.

Outbreak analytics: a developing data science for informing the response to emerging pathogens

Despite continued efforts to improve health systems worldwide, emerging pathogen epidemics remain a major public health concern. Effective response to such outbreaks relies on timely intervention, ideally informed by all available sources of data. The collection, visualization and analysis of outbreak data are becoming increasingly complex, owing to the diversity in types of data, questions and available methods to address them. Recent advances have led to the rise of outbreak analytics, an emerging data science focused on the technological and methodological aspects of the outbreak data pipeline, from collection to analysis, modelling and reporting to inform outbreak response. In this article, we assess the current state of the field. After laying out the context of outbreak response, we critically review the most common analytics components, their inter-dependencies, data requirements and the type of information they can provide to inform operations in real time. We discuss some challenges and opportunities and conclude on the potential role of outbreak analytics for improving our understanding of, and response to outbreaks of emerging pathogens.

This article is part of the theme issue ‘Modelling infectious disease outbreaks in humans, animals and plants: epidemic forecasting and control‘. This theme issue is linked with the earlier issue ‘Modelling infectious disease outbreaks in humans, animals and plants: approaches and important themes’.

Tracking progress towards malaria elimination in China: Individual-level estimates of transmission and its spatiotemporal variation using a diffusion network approach

In order to monitor progress towards malaria elimination, it is crucial to be able to measure changes in spatio-temporal transmission. However, common metrics of malaria transmission such as parasite prevalence are under powered in elimination contexts. China has achieved major reductions in malaria incidence and is on track to eliminate, having reporting zero locally-acquired malaria cases in 2017 and 2018. Understanding the spatio-temporal pattern underlying this decline, especially the relationship between locally-acquired and imported cases, can inform efforts to maintain elimination and prevent re-emergence. This is particularly pertinent in Yunnan province, where the potential for local transmission is highest. Using a geo-located individual-level dataset of cases recorded in Yunnan province between 2011 and 2016, we introduce a novel Bayesian framework to model a latent diffusion process and estimate the joint likelihood of transmission between cases and the number of cases with unobserved sources of infection. This is used to estimate the case reproduction number, Rc. We use these estimates within spatio-temporal geostatistical models to map how transmission varied over time and space, estimate the timeline to elimination and the risk of resurgence. We estimate the mean Rc between 2011 and 2016 to be 0.171 (95% CI = 0.165, 0.178) for P. vivax cases and 0.089 (95% CI = 0.076, 0.103) for P. falciparum cases. From 2014 onwards, no cases were estimated to have a Rc value above one. An unobserved source of infection was estimated to be moderately likely (p>0.5) for 19/ 611 cases and high (p>0.8) for 2 cases, suggesting very high levels of case ascertainment. Our estimates suggest that, maintaining current intervention efforts, Yunnan is unlikely to experience sustained local transmission up to 2020. However, even with a mean of 0.005 projected up to 2020, locally-acquired cases are possible due to high levels of importation.

epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease. epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.

epiflows: an R package for risk assessment of travel-related spread of disease

As international travel increases worldwide, new surveillance tools are needed to help identify locations where diseases are most likely to be spread and prevention measures need to be implemented. In this paper we present epiflows, an R package for risk assessment of travel-related spread of disease.  epiflows produces estimates of the expected number of symptomatic and/or asymptomatic infections that could be introduced to other locations from the source of infection. Estimates (average and confidence intervals) of the number of infections introduced elsewhere are obtained by integrating data on the cumulative number of cases reported, population movement, length of stay and information on the distributions of the incubation and infectious periods of the disease. The package also provides tools for geocoding and visualization. We illustrate the use of epiflows by assessing the risk of travel-related spread of yellow fever cases in Southeast Brazil in December 2016 to May 2017.