On some fundamental challenges in monitoring epidemics

Epidemic models often reflect characteristic features of infectious spreading processes by coupled nonlinear differential equations considering different states of health (such as susceptible, infectious or recovered). This compartmental modelling approach, however, delivers an incomplete picture of the dynamics of epidemics, as it neglects stochastic and network effects, and the role of the measurement process, on which the estimation of epidemiological parameters and incidence values relies. In order to study the related issues, we combine established epidemiological spreading models with a measurement model of the testing process, considering the problems of false positives and false negatives as well as biased sampling. Studying a model-generated ground truth in conjunction with simulated observation processes (virtual measurements) allows one to gain insights into the fundamental limitations of purely data-driven methods when assessing the epidemic situation. We conclude that epidemic monitoring, simulation, and forecasting are wicked problems, as applying a conventional data-driven approach to a complex system with nonlinear dynamics, network effects and uncertainty can be misleading. Nevertheless, some of the errors can be corrected for, using scientific knowledge of the spreading dynamics and the measurement process. We conclude that such corrections should generally be part of epidemic monitoring, modelling and forecasting efforts. This article is part of the theme issue ‘Data science approaches to infectious disease surveillance’.

Characteristics of SARS-CoV-2 Positive Individuals in California From Two Periods During Notable Decline in Incident Infection

Despite declining SARS-CoV-2 incidence, continued epidemic monitoring is warranted. We collected SARS-CoV-2 test results from 150 drive-through testing centers across California from two observation periods: February 23rd-March 3rd 2021 and April 15th-April 30th 2021. We assessed SARS-CoV-2 positivity, stratified by Hispanic heritage among sociodemographic characteristics and potential exposures. We analyzed 114,789 test results (5.1% and 2.6% positive during the respective observation periods). Nearly half of all positive tests were among testers reporting a recent exposure (48.8% and 45.3% during the respective observation periods). Those findings may provide insight into evolving local transmission dynamics and support targeted public health strategies.

Quantitative detection of SARS-CoV-2 B.1.1.7 variant in wastewater by allele-specific RT-qPCR

Wastewater-based epidemiology (WBE) has emerged as a critical public health tool in tracking the SARS-CoV-2 epidemic. Monitoring SARS-CoV-2 variants of concern in wastewater has to-date relied on genomic sequencing, which lacks sensitivity necessary to detect low variant abundances in diluted and mixed wastewater samples. Here, we develop and present an open-source method based on allele specific RT-qPCR (AS RT-qPCR) that detects and quantifies the B.1.1.7 variant, targeting spike protein mutations at three independent genomic loci highly predictive of B.1.1.7 (HV69/70del, Y144del, and A570D). Our assays can reliably detect and quantify low levels of B.1.1.7 with low cross-reactivity, and at variant proportions between 0.1% and 1% in a background of mixed SARS-CoV-2. Applying our method to wastewater samples from the United States, we track B.1.1.7 occurrence over time in 19 communities. AS RT-qPCR results align with clinical trends, and summation of B.1.1.7 and wild-type sequences quantified by our assays strongly correlate with SARS-CoV-2 levels indicated by the US CDC N1/N2 assay. This work paves the path for rapid inexpensive surveillance of B.1.1.7 and other SARS-CoV-2 variants in wastewater.

Prediction of hot spot areas of hemorrhagic fever with renal syndrome in Hunan Province based on an information quantity model and logistical regression model

Background China’s “13th 5-Year Plan” (2016–2020) for the prevention and control of sudden acute infectious diseases emphasizes that epidemic monitoring and epidemic focus surveys in key areas are crucial for strengthening national epidemic prevention and building control capacity. Establishing an epidemic hot spot areas and prediction model is an effective means of accurate epidemic monitoring and surveying. Objective: This study predicted hemorrhagic fever with renal syndrome (HFRS) epidemic hot spot areas, based on multi-source environmental variable factors. We calculated the contribution weight of each environmental factor to the morbidity risk, obtained the spatial probability distribution of HFRS risk areas within the study region, and detected and extracted epidemic hot spots, to guide accurate epidemic monitoring as well as prevention and control. Methods: We collected spatial HFRS data, as well as data on various types of natural and human social activity environments in Hunan Province from 2010 to 2014. Using the information quantity method and logistic regression modeling, we constructed a risk-area-prediction model reflecting the epidemic intensity and spatial distribution of HFRS. Results: The areas under the receiver operating characteristic curve of training samples and test samples were 0.840 and 0.816. From 2015 to 2019, HRFS case site verification showed that more than 82% of the cases occurred in high-risk areas. Discussion This research method could accurately predict HFRS hot spot areas and provided an evaluation model for Hunan Province. Therefore, this method could accurately detect HFRS epidemic high-risk areas, and effectively guide epidemic monitoring and surveyance.