Patient characteristics and public health office factors associated with long reporting delay of COVID-19 cases in Sapporo City, Japan.

BACKGROUND: For therapeutic efficacy, molnupiravir and nirmatrelvir-ritonavir must be started to treat patients within 5 days of disease onset to treat patients with COVID-19. However, some patients spend more than 5 days from disease onset before reporting to the Public Health Office. This study aimed to clarify the characteristics of patients with reporting delay. METHODS: This study included data from 12,399 patients with COVID-19 who reported to the Public Health Office from March 3rd, 2021 to June 30th, 2021. Patients were stratified into "linked" (n=7,814) and "unlinked" (n=4,585) cases depending on whether they were linked to other patients. A long reporting delay was defined as the difference between the onset and reporting dates of 5 days or more. Univariate and multivariate analyses were performed using log-binomial regression to identify factors related to long reporting delay, and prevalence ratios with corresponding 95% confidence intervals were calculated. RESULTS: The proportion of long reporting delay was 24.4% (1904/7814) and 29.3% (1344/4585) in linked and unlinked cases, respectively. Risks of long reporting delay among linked cases were living alone and onset on the day with a higher 7-day daily average confirmed cases or onset on weekends; whereas, risks for unlinked cases were age over 65 years, without occupation and living alone. CONCLUSION: Our results suggest the necessity to establish a Public Health Office system that is less susceptible to the rapid increase in the number of patients, promotes educational activities for people with fewer social connections, and improves access to health care.

Analysis of reporting lag in daily data of COVID-19 in Japan.

The daily announcement of positive COVID-19 cases had a major socioeconomic impact. In Japan, it is well known that the characteristic of this number as time series data is the weekly periodicity. We assume that this periodicity is generated by changes in the timing of reporting on the weekend. We analyze a lag structure that shows how congestion that occurs over the weekend affects the number of new confirmed cases at the beginning of the following week. We refer to this reporting delay as the weekend effect. Our study aims to describe the geographical heterogeneity found in the time series of reported positive cases. We use data on the number of new positives reported by the prefectures. Our results suggest that delays generally occur in prefectures with a population of more than 2 million, including Japan's three largest metropolitan areas, Tokyo, Osaka, and Nagoya. The number of new positives was higher in the more populated prefectures. This will explain the weekend effect.

Nowcasting COVID-19 deaths in England by age and region.

Understanding the trajectory of the daily number of COVID-19 deaths is essential to decisions on how to respond to the pandemic, but estimating this trajectory is complicated by the delay between deaths occurring and being reported. In England the delay is typically several days, but it can be weeks. This causes considerable uncertainty about how many deaths occurred in recent days. Here we estimate the deaths per day in five age strata within seven English regions, using a Bayesian model that accounts for reporting-day effects and longer-term changes in the delay distribution. We show how the model can be computationally efficiently fitted when the delay distribution is the same in multiple strata, for example, over a wide range of ages.

On the use of real-time reported mortality data in modelling and analysis during an epidemic outbreak: [version 1;peer review: 2 approved with reservations]

Background: For diseases like Covid-19, where it has been difficult to identify the true number of infected people, or where the number of known cases is heavily influenced by the number of tests performed, hospitalizations and deaths play a significant role in understanding the epidemic and in determining the appropriate response. However, the Covid-19 deaths data reported by some countries display a significant weekly variability, which can make the interpretation and use of the death data in analysis and modeling difficult. Methods: We derive the mathematical relationship between the series of new daily deaths by reporting date and the series of deaths by death date. We then apply this formalism to the corresponding time-series reported by Sweden during the Covid-19 pandemic. Results: The practice of reporting new deaths daily, as is standard procedure during an outbreak in most countries and regions, should be viewed as a time-dependent filter, modulating the underlying true death curve. After having characterized the Swedish reporting process, we show how smoothing of the Swedish reported daily deaths series results in a curve distinctly different from the true death curve. We also comment on the use of nowcasting methods. Conclusions: Modelers and analysts using the series of new daily deaths by reporting date should take extra care when it is highly variable and when there is a significant reporting delay. It might be appropriate to instead use the series of deaths by death date combined with a nowcasting algorithm as basis for their analysis.

Timeliness of provisional United States mortality data releases during the COVID-19 pandemic: delays associated with electronic death registration system and weekly mortality.

All-cause mortality counts allow public health authorities to identify populations experiencing excess deaths from pandemics, natural disasters, and other emergencies. Delays in the completeness of mortality counts may contribute to misinformation because death counts take weeks to become accurate. We estimate the timeliness of all-cause mortality releases during the COVID-19 pandemic for the dates 3 April to 5 September 2020 by estimating the number of weekly data releases of the NCHS Fluview Mortality Surveillance System until mortality comes within 99% of the counts in the 19 March 19 2021 provisional mortality data release. States' mortality counts take 5 weeks at median (interquartile range 4-7 weeks) to completion. The fastest states were Maine, New Hampshire, Vermont, New York, Utah, Idaho, and Hawaii. States that had not adopted the electronic death registration system (EDRS) were 4.8 weeks slower to achieve complete mortality counts, and each weekly death per 10^8 was associated with a 0.8 week delay. Emergency planning should improve the timeliness of mortality data by improving state vital statistics digital infrastructure.

A novel ORF1a-based SARS-CoV-2 RT-PCR assay to resolve inconclusive samples.

BACKGROUND: India bears the second largest burden of severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. A multitude of reverse transcription polymerase chain reaction (RT-PCR) detection assays with disparate gene targets, including automated high-throughput platforms, are available. Varying concordance and interpretation of diagnostic results in this setting can result in significant reporting delays, leading to suboptimal disease management. This article reports the development of a novel ORF1a-based SARS-CoV-2 RT-PCR assay - Viroselect - that shows high concordance with conventional assays and the ability to resolve inconclusive results generated during the peak of the epidemic in Mumbai, India. METHODS: A unique target region within SARS-CoV-2 ORF1a - the non-structural protein 3 (nsp3) region - was used to design and develop the assay. This hypervariable region (1923-3956) between SARS-CoV-2, SARS-CoV-1 and Middle East respiratory syndrome coronavirus was utilized to design the primers and probes for the RT-PCR assay. The concordance of this assay with commonly used emergency use authorization (US Food and Drug Administration) manual kits and an automated high-throughput testing platform was evaluated. Further, a retrospective analysis was carried out using Viroselect on samples reported as 'inconclusive' between April and October 2020. RESULTS: In total, 701 samples were tested. Concordance analysis of 477 samples demonstrated high overall agreement of Viroselect with both manual (87.6%) and automated (84.7%) assays. Also, in the retrospective analysis of 224 additional samples reported as 'inconclusive', Viroselect was able to resolve 100% (19/19) and 93.7% (192/205) of samples which had inconclusive results on manual and automated high-throughput platforms, respectively. CONCLUSION: Viroselect had high concordance with conventional assays, both manual and automated, and has potential to resolve inconclusive samples.

Forecasting of COVID-19 onset cases: a data-driven analysis in the early stage of delay.

The outbreak of COVID-19 has become a global public health event. Many researchers have proposed many epidemiological models to predict the outbreak trend of COVID-19, but all use confirmed cases to predict "onset cases." In this article, a total of 5434 cases were collected from National Health Commission and other provincial Health Commission in China, spanning from 1 December 2019 to 23 February 2020. We studied the delayed distribution of patients from onset to be confirmed. The delay is divided into two stages, which takes about 15 days or even longer. Therefore, considering the right truncation of the data, we proposed a "predict-in-advance" method, used the number of "visiting hospital cases" to predict the number of "onset cases." The results not only show that our prediction shortens the delay of the second stage, but also the predicted value of onset cases is quite close to the real value of onset cases, which can effectively predict the epidemic trend of sudden infectious diseases, and provide an important reference for the government to formulate control measures in advance.

Real-time monitoring the transmission potential of COVID-19 in Singapore, March 2020.

BACKGROUND: As of March 31, 2020, the ongoing COVID-19 epidemic that started in China in December 2019 is now generating local transmission around the world. The geographic heterogeneity and associated intervention strategies highlight the need to monitor in real time the transmission potential of COVID-19. Singapore provides a unique case example for monitoring transmission, as there have been multiple disease clusters, yet transmission remains relatively continued. METHODS: Here we estimate the effective reproduction number, Rt, of COVID-19 in Singapore from the publicly available daily case series of imported and autochthonous cases by date of symptoms onset, after adjusting the local cases for reporting delays as of March 17, 2020. We also derive the reproduction number from the distribution of cluster sizes using a branching process analysis that accounts for truncation of case counts. RESULTS: The local incidence curve displays sub-exponential growth dynamics, with the reproduction number following a declining trend and reaching an estimate at 0.7 (95% CI 0.3, 1.0) during the first transmission wave by February 14, 2020, while the overall R based on the cluster size distribution as of March 17, 2020, was estimated at 0.6 (95% CI 0.4, 1.02). The overall mean reporting delay was estimated at 6.4 days (95% CI 5.8, 6.9), but it was shorter among imported cases compared to local cases (mean 4.3 vs. 7.6 days, Wilcoxon test, p < 0.001). CONCLUSION: The trajectory of the reproduction number in Singapore underscores the significant effects of successful containment efforts in Singapore, but it also suggests the need to sustain social distancing and active case finding efforts to stomp out all active chains of transmission.