Visual tools to assess the plausibility of algorithm-identified infectious disease clusters: an application to mumps data from the Netherlands dating from January 2009 to June 2016
Abstract:Introduction
With growing amounts of data available, identification of clusters of persons linked to each other by transmission of an infectious disease increasingly relies on automated algorithms. We propose cluster finding to be a two-step process: first, possible transmission clusters are identified using a cluster algorithm, second, the plausibility that the identified clusters represent genuine transmission clusters is evaluated.
Aim
To introduce visual tools to as… Show more
“…From our results, it can be inferred that the effective time span for considering the influence of Wuhan Population flow on epidemic is 4 days. This is more detailed than those studies that have claimed the interval of 3–7 days for taking into account the influence of population flow 1 , 21 , 48 . Figure 5 The correlation coefficients between Wuhan population flow and cumulative confirmed cases, excluding Hubei, considering different time intervals.…”
Section: Resultsmentioning
confidence: 93%
“…The National Health Commission announced that the first confirmed case in Wuhan was on January 10, and Wuhan began to “close the city” on January 23 46 . Taking into account that the incubation period of the COVID-19 is around 0–14 days 1 , 21 , 47 , 48 , we calculated the correlation coefficients between the cumulative confirmed cases, excluding Hubei, and the population flow with the interval of 0–14 days from January 10 to February 6 (January 23 plus 14 days), with the interval of 0–14 days. As can be seen in Fig.…”
In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China’s non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics.
“…From our results, it can be inferred that the effective time span for considering the influence of Wuhan Population flow on epidemic is 4 days. This is more detailed than those studies that have claimed the interval of 3–7 days for taking into account the influence of population flow 1 , 21 , 48 . Figure 5 The correlation coefficients between Wuhan population flow and cumulative confirmed cases, excluding Hubei, considering different time intervals.…”
Section: Resultsmentioning
confidence: 93%
“…The National Health Commission announced that the first confirmed case in Wuhan was on January 10, and Wuhan began to “close the city” on January 23 46 . Taking into account that the incubation period of the COVID-19 is around 0–14 days 1 , 21 , 47 , 48 , we calculated the correlation coefficients between the cumulative confirmed cases, excluding Hubei, and the population flow with the interval of 0–14 days from January 10 to February 6 (January 23 plus 14 days), with the interval of 0–14 days. As can be seen in Fig.…”
In December 2019, corona virus disease 2019 (COVID-19) has broken out in China. Understanding the distribution of disease at the national level contributes to the formulation of public health policies. There are several studies that investigating the influencing factors on distribution of COVID-19 in China. However, more influencing factors need to be considered to improve our understanding about the current epidemic. Moreover, in the absence of effective medicine or vaccine, the Chinese government introduced a series of non-pharmaceutical interventions (NPIs). However, assessing and predicting the effectiveness of these interventions requires further study. In this paper, we used statistical techniques, correlation analysis and GIS mapping expression method to analyze the spatial and temporal distribution characteristics and the influencing factors of the COVID-19 in mainland China. The results showed that the spread of outbreaks in China’s non-Hubei provinces can be divided into five stages. Stage I is the initial phase of the COVID-19 outbreak; in stage II the new peak of the epidemic was observed; in stage III the outbreak was contained and new cases decreased; there was a rebound in stage IV, and stage V led to level off. Moreover, the cumulative confirmed cases were mainly concentrated in the southeastern part of China, and the epidemic in the cities with large population flows from Wuhan was more serious. In addition, statistically significant correlations were found between the prevalence of the epidemic and the temperature, rainfall and relative humidity. To evaluate the NPIs, we simulated the prevalence of the COVID-19 based on an improved SIR model and under different prevention intensity. It was found that our simulation results were compatible with the observed values and the parameter of the time function in the improved SIR model for China is a = − 0.0058. The findings and methods of this study can be effective for predicting and managing the epidemics and can be used as an aid for decision makers to control the current and future epidemics.
“…The gold standard for validating clusters of disease is an epidemiological link between cases. 36 Although some mpox case report forms in our study list contacts, identifying those contacts is challenging. The identification of mpox clusters in our study could be improved with more available data types, such as social, ecological, and genetic data.…”
Section: Discussionmentioning
confidence: 99%
“…We searched the Program for Monitoring Emerging Diseases (ProMED) and Google Scholar for documentation of any outbreaks of mpox in Tshuapa Province that may have occurred during the study period. To further evaluate the plausibility of identified clusters, we compared the calculated pairwise differences by data type and cluster 36 for singletons and clusters with 10 or more cases. We would expect clusters to have smaller pairwise distances compared to the distribution of pairwise distances for singletons.…”
Incidence of human mpox has been increasing in West and Central Africa, including in the Democratic Republic of Congo (DRC), where monkeypox virus (MPXV) is endemic. Most estimates of the pathogens transmissibility in DRC are based on data from the 1980s. Amid the global 2022 mpox outbreak, new estimates are needed to characterize the epidemic potential of the virus and inform outbreak control strategies. We used the R package vimes to identify clusters of laboratory-confirmed mpox cases in Tshuapa Province, DRC. Cases with both temporal and spatial data were assigned to clusters based on the serial interval and spatial kernel of the disease. We used the size of the clusters to infer the effective reproduction number, Rt, and the rate of zoonotic spillover of MPXV into the human population. Out of 1,463 confirmed mpox cases reported in Tshuapa Province between 2013 and 2017, 878 had both date of symptom onset and a location with geographic coordinates. Results include an estimated Rt of 0.82 (95% CI: 0.79 – 0.85) and a rate of 132 (95% CI: 122 – 143) spillovers per year assuming a reporting rate of 0.25. This estimate of Rt is larger compared to most previous estimates. One potential explanation for this result is that Rt could have increased in DRC over time due to declining population-level immunity conferred by smallpox vaccination, which was discontinued around 1982. Rt could be overestimated if our assumption of one spillover event per cluster does not hold. Our results are consistent with increased transmissibility of MPXV in Tshuapa Province.
“…Conventional clustering analyses are typically based on pairwise genetic distances or partitions of phylogenetic trees ( 25 , 26 ) with some degree of manual curation to ensure cluster plausibility and stability, and incorporation of epidemiological data ( 27 ). Our clustering approaches are intended to identify situations where a more thorough investigation would likely discover the outbreaks, while remaining completely automated and restricted to pairwise distance data.…”
Genomic surveillance of SARS-CoV-2 has been essential to inform public health response to outbreaks. The high incidence of infection has resulted in a smaller proportion of cases undergoing whole genome sequencing due to finite resources. We present a framework for estimating the impact of reduced depths of genomic surveillance on the resolution of outbreaks, based on a clustering approach using pairwise genetic and temporal distances. We apply the framework to simulated outbreak data to show that outbreaks are detected less frequently when fewer cases are subjected to whole genome sequencing. The impact of sequencing fewer cases depends on the size of the outbreaks, and on the genetic and temporal similarity of the index cases of the outbreaks. We also apply the framework to an outbreak of the SARS-CoV-2 Delta variant in New South Wales, Australia. We find that the detection of clusters in the outbreak would have been delayed if fewer cases had been sequenced. Existing recommendations for genomic surveillance estimate the minimum number of cases to sequence in order to detect and monitor new virus variants, assuming representative sampling of cases. Our method instead measures the resolution of clustering, which is important for genomic epidemiology, and accommodates sampling biases.
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