Average Number Of Secondary Cases Research Articles

Monitoring the reproductive number of COVID-19 in France: Comparative estimates from three datasets.

The effective reproduction number (Rt) quantifies the average number of secondary cases caused by one person with an infectious disease. Near-real-time monitoring of Rt during an outbreak is a major indicator used to monitor changes in disease transmission and assess the effectiveness of interventions. The estimation of Rt usually requires the identification of infected cases in the population, which can prove challenging with the available data, especially when asymptomatic people or with mild symptoms are not usually screened. The purpose of this study was to perform sensitivity analysis of Rt estimates for COVID-19 surveillance in France based on three data sources with different sensitivities and specificities for identifying infected cases. We applied a statistical method developed by Cori et al. to estimate Rt using (1) confirmed cases identified from positive virological tests in the population, (2) suspected cases recorded by a national network of emergency departments, and (3) COVID-19 hospital admissions recorded by a national administrative system to manage hospital organization. Rt estimates in France from May 27, 2020, to August 12, 2022, showed similar temporal trends regardless of the dataset. Estimates based on the daily number of confirmed cases provided an earlier signal than the two other sources, with an average lag of 3 and 6 days for estimates based on emergency department visits and hospital admissions, respectively. The COVID-19 experience confirmed that monitoring temporal changes in Rt was a key indicator to help the public health authorities control the outbreak in real time. However, gaining access to data on all infected people in the population in order to estimate Rt is not straightforward in practice. As this analysis has shown, the opportunity to use more readily available data to estimate Rt trends, provided that it is highly correlated with the spread of infection, provides a practical solution for monitoring the COVID-19 pandemic and indeed any other epidemic.

Monitoring the reproduction number of COVID-19 in France: estimates compared from 3 datasets

Abstract Background The effective reproduction number (Rt) represents the average number of secondary cases generated by an infected person. During an outbreak, near-real-time monitoring of Rt constitutes a key indicator for detecting changes in disease transmission and assessing the effectiveness of interventions. The estimation of Rt usually requires identifying infected cases in the population which is in practice challenging from available data. The purpose of this study was to compare Rt estimates for COVID-19 surveillance in France based on three data sources of different sensitivity and specificity for identifying infected cases. Methods By applying a statistical method developed by Cori et al., we estimated Rt using (1) confirmed cases identified from positive virological tests among the tested population (2) suspected cases recorded by a national network of emergency departments (3) hospital admissions for COVID-19 recorded by a national administrative system to manage hospital's organization. Results From June 2020 to March 2022, the estimates of Rt in France showed similar temporal trends regardless of the dataset. Estimates based on the daily number of confirmed cases provided an earlier signal that the two other sources, with a lag of 3 and 6 days compared to estimates based on emergency department visits and hospital admissions, respectively. Conclusions The COVID-19 experience has proven that monitoring temporal changes in Rt was a key indicator to help public health authorities controlling the outbreak in real time. Having data on infected people in the population to estimate the Rt is not straightforward in practice. As this study has shown, the opportunity of using more readily available data, provided that it is highly correlated with the spread of infection, gives a practical solution for monitoring the COVID-19 epidemic and any epidemic in general. Key messages • The effective reproduction number (Rt) is a key parameter to monitor transmission during epidemics but its estimation from available data is often a critical issue. • Based on COVID-19 experience, data sufficiently correlated with the spread of infection may be appropriate to estimate Rt and monitor its temporal trend.

Estimating the generation interval from the incidence rate, the optimal quarantine duration and the efficiency of fast switching periodic protocols for COVID-19

The transmissibility of an infectious disease is usually quantified in terms of the reproduction number R_t representing, at a given time, the average number of secondary cases caused by an infected individual. Recent studies have enlightened the central role played by w(z), the distribution of generation times z, namely the time between successive infections in a transmission chain. In standard approaches this quantity is usually substituted by the distribution of serial intervals, which is obtained by contact tracing after measuring the time between onset of symptoms in successive cases. Unfortunately, this substitution can cause important biases in the estimate of R_t. Here we present a novel method which allows us to simultaneously obtain the optimal functional form of w(z) together with the daily evolution of R_t, over the course of an epidemic. The method uses, as unique information, the daily series of incidence rate and thus overcomes biases present in standard approaches. We apply our method to one year of data from COVID-19 officially reported cases in the 21 Italian regions, since the first confirmed case on February 2020. We find that w(z) has mean value {overline{z}} simeq 6 days with a standard deviation sigma simeq 1 day, for all Italian regions, and these values are stable even if one considers only the first 10 days of data recording. This indicates that an estimate of the most relevant transmission parameters can be already available in the early stage of a pandemic. We use this information to obtain the optimal quarantine duration and to demonstrate that, in the case of COVID-19, post-lockdown mitigation policies, such as fast periodic switching and/or alternating quarantine, can be very efficient.

Estimation of R(t) based on illness onset data: An analysis of 1907–1908 smallpox epidemic in Tokyo

R(t), the actual average number of secondary cases per primary case at calendar time t, is epidemiologically useful in assessing transmission dynamics in a population with varying susceptibility levels. However, a technical limitation of existing methods of estimating R(t) is the reliance on the daily number of cases with illness onset and the distribution of the serial interval, although the estimator of R(t) should be calculated as the ratio of newly infected cases at time t to the total number of potentially infectious people at the same time. Using historical data of a smallpox outbreak in Tokyo City, Japan, approximately 100 years ago, we propose a new method to compute R(t) that can be estimated using information on illness onset. Our method decomposes the mechanism of transmission into two distinct pieces of information: the frequency of secondary transmission relative to disease age and the probability density function of the incubation period. Employing a piecewise constant model, our maximum likelihood estimates of R(t) dropped below unity (0.6; 95% confidence interval: 0.5–0.7) for the period from Day 64 to Day 79, indicating that the epidemic was under control in this period. R(t) was continuously below one through the remaining days. The model prediction captured the overall observed pattern of the epidemic well. Our method is appropriate for acute infectious diseases other than smallpox for which variations in infectivity relative to disease age should be considered to correctly estimate the transmission potential, such as the ongoing global epidemic of coronavirus disease 2019 (COVID-19).

Explaining the effective reproduction number of COVID-19 through mobility and enterprise statistics: Evidence from the first wave in Japan.

This study uses mobility statistics combined with business census data for the eight Japanese prefectures with the highest coronavirus disease-2019 (COVID-19) infection rates to study the effect of mobility reductions on the effective reproduction number (i.e., the average number of secondary cases caused by one infected person). Mobility statistics are a relatively new data source created by compiling smartphone location data; they can be effectively used for understanding pandemics if integrated with epidemiological findings and other economic data sets. Based on data for the first wave of infections in Japan, we found that reductions targeting the hospitality industry were slightly more effective than restrictions on general business activities. Specifically, we found that to hold back the pandemic (that is, to reduce the effective reproduction number to one or less for all days), a 20%–35% reduction in weekly mobility is required, depending on the region. A lesser goal, 80% of days with one or less observed transmission, can be achieved with a 6%–30% reduction in weekly mobility. These are the results if other potential causes of spread are ignored; for a fuller picture, more careful observations, expanded data sets, and advanced statistical modeling are needed.

Mask Up: Academic-Community-Government Partnerships to Advance Public Health During COVID-19.

Mask Up: Academic-Community-Government Partnerships to Advance Public Health During COVID-19.

Sensitivity and mathematical model analysis on secondhand smoking tobacco

In this paper, we are concerned with a mathematical model of secondhand smoker. The model is biologically meaningful and mathematically well posed. The reproductive number R_{0} is determined from the model, and it measures the average number of secondary cases generated by a single primary case in a fully susceptible population. If R_{0}<1, the smoking-free equilibrium point is stable, and if R_{0}>1, endemic equilibrium point is unstable. We also provide numerical simulation to show stability of equilibrium points. In addition, sensitivity analysis of parameters involving in the dynamic system of the proposed model has been included. The parameters involving in reproductive number measure the relative change in R_{0} when the value of the parameter changes.

Perioperative management of patients with suspected or severe infection with SARS-CoV-2 coronavirus programmed for the implementation of electronic devices for the control of chronic pain

At the end of 2019, a series of patients affected by lung infection of initially unknown aetiology with clinical presentations very similar to viral pneumonia were registered in China. Sequencing analysis of samples from the lower respiratory tract identified a new type of virus from the family Coronaviridae called SARS-CoV-2 as the causative agent of the outbreak; the agent responsible for the disease that was renamed COVID-19.1 Since then, millions of cases have been identified around the world. The World Health Organization (WHO) initially declared the infection to be a Public Health Emergency of International Concern, and later classed it as a pandemic.2, 3 The vast majority of coronaviruses are responsible for mild upper respiratory tract infections in immunocompetent adults, and can cause more serious conditions, such as severe acute respiratory distress syndrome (ARDS) and sepsis in patients with risk factors, namely, cardiovascular disease (10.5%), diabetes (7.3%), chronic respiratory disease (6.3%), high blood pressure (6%) and cancer (5.6%).1, 2 Person-to-person transmission occurs mainly by respiratory droplets and by contact with contaminated material through mucosa (oral, ocular and nasal). It can also be transmitted by aerosols in aerosol-generating therapeutic procedures. The average number of secondary cases produced from 1 case has been estimated at between 2 and 3,2, 4 while the average incubation period is between 5.2 and 12.5 days, although in some cases it can be as long as 24 days.1 The potential repercussions of the disease have led international, regional and local health organizations to adopt a series of measures to deal with COVID-19 and try to reduce its impact5 not only in society as a whole, but also in healthcare. These measures include changing guidelines and promoting strategies such as telemedicine,6 a service that has been introduced in different specialties, including Pain Treatment Units. Telemedicine is basically applicable in routine, elective, non-urgent medical consultations (clinical evolution, start and follow-up of medical treatments, psychological intervention techniques, etc.).6 In certain cases, however, such as patients with chronic pain refractory to medical treatment who need invasive therapeutic procedures, telemedicine services are ineffective because they exclude direct patient-doctor contact, and with it, the risk of virus transmission. In this context, intrathecal drug infusion (IDI) pumps and both spinal cord (SCS) and peripheral nerve (PNS) stimulation device are now frequently used as part of the treatment strategy in patients with chronic pain refractory to other therapies.7 SCSs have now been approved by the US Food and Drug Administration (FDA) for the treatment of chronic neuropathic pain of the spine and limbs, post-surgical spine syndrome, and complex regional pain syndrome. However, they have also been used successfully in many other indications, including chronic intractable angina, peripheral arterial disease, peripheral neuropathic pain, and in the treatment of visceral pain.7, 8, 9 In IDIs, the intrathecal administration of baclofen is approved by the FDA for the treatment of spasticity, and the intrathecal administration of morphine and ziconotide is approved for the treatment of chronic, refractory, nociceptive, neuropathic or mixed cancer or non-cancer-related pain.7, 10 In light of the current situation, we believe it is important to establish guidelines for the management of patients with suspected or serious infection by Coronavirus SARS-CoV-2 scheduled for implantation of electronic systems for the control of chronic pain (IDIs and SCSs). The purpose of these recommendations is to reduce the spread of COVID-19 among both medical personnel and the general population, and they may be updated as the pandemic progresses and new evidence emerges.

A Review of Reproductive Number of Pandemic Covid-19: Comparative Analysis of R Value

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is one of the most devastating outbreaks witnessed in the last 100 years causing a global health concern.At the beginning of a COVID-19 infection caused by the novel coronavirus (SARS-CoV-2), there is a period of time known as the latency period. In order to epidemic progression many scientists have concentrated on calculating the average number of secondary cases caused by a primary case in the susceptible population, which is referred as basic reproductive number, Ro. In this paper, we have studied the effect of basic reproductive number on the outbreak situation as well as comprehended the transmission pattern of COVID-19 globally. We have analyzed several data of basic reproductive numbers to discuss and finally, capable of exhibiting the prediction of this disease. Finally, comparison of reproductive numbers for several countries are represented graphically for better understanding the present outbreak situation of pandemic, COVID-19.

Elementi metodologici per una riflessione sui dati dell’epidemia di sars-cov-2

Findings of the seroprevalence survey conducted by Istat between May 25 and July 15 2020, on a sample of 64,660 people, show that only 2.5% of Italian people developed antibodies to SARS-CoV-2, a prevalence very far from the hypothesis of achieving herd immunity. Starting from the comment on these results, we summarized some of the main indicators used to evaluate the epidemic curves (R, R0, Rt) and the concept of herd immunity. R0, basic reproduction number, represents the average number of secondary cases we expect to observe from a single primary case in a population with no immunity to the disease before prevention and containment measures have been planned. Rt, effective reproduction number, is calculated over time and considers how the outbreak progresses, as a result of the containment measures and of people who might have gained immunity, because they survived from infection or were vaccinated. We presented the issue of herd immunity, or community immunity, a state of protection in a population obtained because the number of people in the population who are immune to infectious disease is above a critical threshold, resulting in a protection even for those who are not immune.

Digital contact tracing technologies in epidemics: a rapid review.

Digital contact tracing technologies in epidemics: a rapid review.

A Primer on COVID‐19 Mathematical Models

The emergence of severe acute respiratory syndrome coronavirus 2 (or coronavirus disease [COVID]-19) has led to a widespread global pandemic ((1)). COVID-19 symptoms and mortality are disproportionately more severe in people with obesity and obesity-related comorbidities ((2, 3)). This is of concern for the United States, where ~42% have obesity, and of these, 85% have type 2 diabetes. Without effective vaccines or therapies, there is an urgent need for prevention and treatment strategies for COVID-19 infections. Developing such strategies requires a thorough understanding of how COVID-19 infections spread in a population. Mathematical modeling predicts the spread of COVID-19 and permits testing different strategies to control and reduce population disease impacts. The New York Times published COVID-19 predictions from five mathematical models with widely varying results ((4)). A recent review ((5)) also highlighted model differences and noted that a fluid situation dependent on many external variables should not be viewed as a “crystal ball.” Why do COVID-19 models yield such variable predictions? How are the different COVID-19 models developed? How should models be applied? Here, we provide a brief tutorial addressing these questions. The majority of models fall into the following two categories, which are applied for different purposes: projections versus statistical forecasts. Projections are deterministic and they explain what could happen under a set of underlying hypotheses, while statistical forecasts use observed data to predict what will happen ((6)). In 1927, Kermack and McKendrick ((7)) developed the first continuous variable projection model of epidemic population dynamics, often referred to as a “susceptible, infected, and removed” (SIR) model. They compartmentalized a constant population into three states. The first state represents individuals susceptible to the disease with the number of susceptible individuals on day of the epidemic, denoted by The second state is the number of infected individuals on day of the epidemic, denoted by Finally, individuals removed from the infectious disease dynamics through death or recovery with immunity on day of the epidemic are denoted by The key property of projection models like the Kermack-McKendrick system is that they are based on logical assumptions of the underlying mechanics of a process and they can be developed without data to immediately address “what if” questions. Here, r declines as the number of infected individuals gets higher, reflecting increased social distancing during peak infectivity. On the other hand, increases when the number of infected individuals decrease. The values , K, and n are parameters that could be fit to data once available. The changes in assumptions alter the projections as depicted in Figure 2. Once data is fit to model parameters, SIR models can predict when infections peak or how high the peak may be, but as pointed out in Jewell et al. ((5)) and observed in Figure 2, these are dependent on the underlying model assumptions. Forecasting models are more useful after data has been gathered. For instance, most SIR COVID-19 models use fixed parameters, which lead to a constant referred to as or “ naught.” is defined as the number of secondary infections that will result from a single infected individual being introduced into a completely susceptible population. A calculated reproduction number during an ongoing epidemic is the “effective” reproduction number, , which is the observed average number of secondary cases from a single primary case per unit time. accounts for changes to through control measures such as social distancing. Within the SIR framework, , where is the time to recover and is the population size. Absent of interventions, the estimated of COVID-19 is between 1.5 and 6.7 ((9)). However, this value is not constant but changing daily. Renewal equations allow us to estimate values ((10)) of that can be fit to a statistical model. Similar to Massad et al. ((6)), we fit an exponential decay model to New York’s data yielding , though other statistical models such as one with an asymptote could be considered. Figure 3 depicts the fitted effective reproduction number versus the calculated values for New York. This forecast model will inform improved SIR model projections and generate prediction intervals. Projection models for the SARS-1 epidemic overestimate compared with statistical forecasting estimates of reproduction numbers ((6)). It is important that researchers use projection models for hypothetical scenario development and statistical forecasting models for forecasting, which are two different goals. Including the differential effects of COVID-19 on individuals with obesity into both projection and forecasting models by separating each compartment into normal weight and obesity populations will be key to understanding the population-wide impacts of COVID-19 on obesity. Obesity researchers can apply both types of models to evaluate prevention and treatment strategies for COVID-19 in persons with obesity.

Reconciling early-outbreak estimates of the basic reproductive number and its uncertainty: framework and applications to the novel coronavirus (SARS-CoV-2) outbreak.

A novel coronavirus (SARS-CoV-2) emerged as a global threat in December 2019. As the epidemic progresses, disease modellers continue to focus on estimating the basic reproductive number —the average number of secondary cases caused by a primary case in an otherwise susceptible population. The modelling approaches and resulting estimates of during the beginning of the outbreak vary widely, despite relying on similar data sources. Here, we present a statistical framework for comparing and combining different estimates of across a wide range of models by decomposing the basic reproductive number into three key quantities: the exponential growth rate, the mean generation interval and the generation-interval dispersion. We apply our framework to early estimates of for the SARS-CoV-2 outbreak, showing that many estimates are overly confident. Our results emphasize the importance of propagating uncertainties in all components of , including the shape of the generation-interval distribution, in efforts to estimate at the outset of an epidemic.

Approaches to Daily Monitoring of the SARS-CoV-2 Outbreak in Northern Italy.

Italy was the first European country affected by the Sars-Cov-2 pandemic, with the first autochthonous case identified on Feb 21st. Specific control measures restricting social contacts were introduced by the Italian government starting from the beginning of March. In the current study we analyzed public data from the four most affected Italian regions. We (i) estimated the time-varying reproduction number (Rt), the average number of secondary cases that each infected individual would infect at time t, to monitor the positive impact of restriction measures; (ii) applied the generalized logistic and the modified Richards models to describe the epidemic pattern and obtain short-term forecasts. We observed a monotonic decrease of Rt over time in all regions, and the peak of incident cases ~2 weeks after the implementation of the first strict containment measures. Our results show that phenomenological approaches may be useful to monitor the epidemic growth in its initial phases and suggest that costly and disruptive public health controls might have had a positive impact in limiting the Sars-Cov-2 spread in Northern Italy.

Estimates of the reproductive numbers and demographic reconstruction of outbreak associated with C:P1.5–1,10–8:F3–6:ST–11(cc11) Neisseria meningitidis strains

BackgroundNeisseria meningitidis can cause sporadic cases and outbreaks of invasive disease, including meningitis and sepsis. The meningococcal serogroup C (MenC) is the second most common serogroup in Italy after MenB. In this study we have estimated the reproductive numbers and the demographic reconstruction on the genomes of invasive N. meningitidis C:P1.5–1,10–8:F3–6:ST–11(cc11) strains isolated in Italy in 2012 – 2017, a period that includes the outbreak in Tuscany. MethodsThe genomes of N. meningitidis were sequenced using the Illumina MiSeq platform, through the whole genome sequencing (WGS) method and were analyzed by the core genome MLST (cgMLST) approach, using the BIGSdb Genome Comparator tool implemented on the PubMLST website. A Bayesian method was applied to study population dynamics across the entire N. meningitidis dataset. The basic reproduction number R0, which indicates the average number of secondary cases generated by a single primary case, was calculated using a Bayesian method, on the dataset and on the two subsets. The effective reproduction number R(t), defined as the average number of secondary cases per infectious case in a population, made up of susceptible and non-susceptible hosts was studied on the Tuscany dataset, with a Bayesian method. ResultsAn increase in the effective number of the N. meningitidis infections was observed between 2013 and 2016. The estimated R0 parameter was 1.31 (95% HPD: 1.03 – 1.64), 1.22 (95% HPD: 0.90 – 1.64) and 1.4 (95% HPD: 0.91 – 1.9) for the entire dataset, first and second subset, respectively. The BDSKY estimated an initial R(t) of about 2.0 (95% HPD: 0.04 – 5.0), which showed a growing trend at the end of 2014, reaching an average value of 3.22 in 2015, and then declining below 1 from the year 2016. ConclusionMonitoring the effective reproduction number can help to inform future vaccination strategies. The increase in the reproductive number for the Tuscany dataset, was consistent with the amplification event that led to the Tuscany outbreak. Subsequently, the intervention that led to the decline of the cases was followed, suggesting a high effectiveness of the vaccination campaign.

Effectiveness of interventions targeting air travellers for delaying local outbreaks of SARS-CoV-2.

BackgroundWe evaluated if interventions aimed at air travellers can delay local severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) community transmission in a previously unaffected country.MethodsWe simulated infected air travellers arriving into countries with no sustained SARS-CoV-2 transmission or other introduction routes from affected regions. We assessed the effectiveness of syndromic screening at departure and/or arrival and traveller sensitisation to the COVID-2019-like symptoms with the aim to trigger rapid self-isolation and reporting on symptom onset to enable contact tracing. We assumed that syndromic screening would reduce the number of infected arrivals and that traveller sensitisation reduces the average number of secondary cases. We use stochastic simulations to account for uncertainty in both arrival and secondary infections rates, and present sensitivity analyses on arrival rates of infected travellers and the effectiveness of traveller sensitisation. We report the median expected delay achievable in each scenario and an inner 50% interval.ResultsUnder baseline assumptions, introducing exit and entry screening in combination with traveller sensitisation can delay a local SARS-CoV-2 outbreak by 8 days (50% interval: 3–14 days) when the rate of importation is 1 infected traveller per week at time of introduction. The additional benefit of entry screening is small if exit screening is effective: the combination of only exit screening and traveller sensitisation can delay an outbreak by 7 days (50% interval: 2–13 days). In the absence of screening, with less effective sensitisation, or a higher rate of importation, these delays shrink rapidly to <4 days.ConclusionSyndromic screening and traveller sensitisation in combination may have marginally delayed SARS-CoV-2 outbreaks in unaffected countries.

Quantifying the impact of physical distance measures on the transmission of COVID-19 in the UK

BackgroundTo mitigate and slow the spread of COVID-19, many countries have adopted unprecedented physical distancing policies, including the UK. We evaluate whether these measures might be sufficient to control the epidemic by estimating their impact on the reproduction number (R0, the average number of secondary cases generated per case).MethodsWe asked a representative sample of UK adults about their contact patterns on the previous day. The questionnaire was conducted online via email recruitment and documents the age and location of contacts and a measure of their intimacy (whether physical contact was made or not). In addition, we asked about adherence to different physical distancing measures. The first surveys were sent on Tuesday, 24 March, 1 day after a “lockdown” was implemented across the UK. We compared measured contact patterns during the “lockdown” to patterns of social contact made during a non-epidemic period. By comparing these, we estimated the change in reproduction number as a consequence of the physical distancing measures imposed. We used a meta-analysis of published estimates to inform our estimates of the reproduction number before interventions were put in place.ResultsWe found a 74% reduction in the average daily number of contacts observed per participant (from 10.8 to 2.8). This would be sufficient to reduce R0 from 2.6 prior to lockdown to 0.62 (95% confidence interval [CI] 0.37–0.89) after the lockdown, based on all types of contact and 0.37 (95% CI = 0.22–0.53) for physical (skin to skin) contacts only.ConclusionsThe physical distancing measures adopted by the UK public have substantially reduced contact levels and will likely lead to a substantial impact and a decline in cases in the coming weeks. However, this projected decline in incidence will not occur immediately as there are significant delays between infection, the onset of symptomatic disease, and hospitalisation, as well as further delays to these events being reported. Tracking behavioural change can give a more rapid assessment of the impact of physical distancing measures than routine epidemiological surveillance.

Quantifying the roles of vomiting, diarrhea, and residents vs. staff in norovirus transmission in U.S. nursing home outbreaks.

The role of individual case characteristics, such as symptoms or demographics, in norovirus transmissibility is poorly understood. Six nursing home norovirus outbreaks occurring in South Carolina, U.S. from 2014 to 2016 were examined. We aimed to quantify the contribution of symptoms and other case characteristics in norovirus transmission using the reproduction number (REi) as an estimate of individual case infectivity and to examine how transmission changes over the course of an outbreak. Individual estimates of REi were calculated using a maximum likelihood procedure to infer the average number of secondary cases generated by each case. The associations between case characteristics and REi were estimated using a weighted multivariate mixed linear model. Outbreaks began with one to three index case(s) with large estimated REi's (range: 1.48 to 8.70) relative to subsequent cases. Of the 209 cases, 155 (75%) vomited, 164 (79%) had diarrhea, and 158 (76%) were nursing home residents (vs. staff). Cases who vomited infected 2.12 (95% CI: 1.68, 2.68) times the number of individuals as non-vomiters, cases with diarrhea infected 1.39 (95% CI: 1.03, 1.87) times the number of individuals as cases without diarrhea, and resident-cases infected 1.53 (95% CI: 1.15, 2.02) times the number of individuals as staff-cases. Index cases tended to be residents (vs. staff) who vomited and infected considerably more secondary cases compared to non-index cases. Results suggest that individuals, particularly residents, who vomit are more infectious and tend to drive norovirus transmission in U.S. nursing home norovirus outbreaks. While diarrhea also plays a role in norovirus transmission, it is to a lesser degree than vomiting in these settings. Results lend support for prevention and control measures that focus on cases who vomit, particularly if those cases are residents.

Estimating the relative probability of direct transmission between infectious disease patients.

Estimating infectious disease parameters such as the serial interval (time between symptom onset in primary and secondary cases) and reproductive number (average number of secondary cases produced by a primary case) are important in understanding infectious disease dynamics. Many estimation methods require linking cases by direct transmission, a difficult task for most diseases. Using a subset of cases with detailed genetic and/or contact investigation data to develop a training set of probable transmission events, we build a model to estimate the relative transmission probability for all case-pairs from demographic, spatial and clinical data. Our method is based on naive Bayes, a machine learning classification algorithm which uses the observed frequencies in the training dataset to estimate the probability that a pair is linked given a set of covariates. In simulations, we find that the probabilities estimated using genetic distance between cases to define training transmission events are able to distinguish between truly linked and unlinked pairs with high accuracy (area under the receiver operating curve value of 95%). Additionally, only a subset of the cases, 10-50% depending on sample size, need to have detailed genetic data for our method to perform well. We show how these probabilities can be used to estimate the average effective reproductive number and apply our method to a tuberculosis outbreak in Hamburg, Germany. Our method is a novel way to infer transmission dynamics in any dataset when only a subset of cases has rich contact investigation and/or genetic data.

Real-Time Estimation of the Risk of Death from Novel Coronavirus (COVID-19) Infection: Inference Using Exported Cases.

The exported cases of 2019 novel coronavirus (COVID-19) infection that were confirmed outside China provide an opportunity to estimate the cumulative incidence and confirmed case fatality risk (cCFR) in mainland China. Knowledge of the cCFR is critical to characterize the severity and understand the pandemic potential of COVID-19 in the early stage of the epidemic. Using the exponential growth rate of the incidence, the present study statistically estimated the cCFR and the basic reproduction number—the average number of secondary cases generated by a single primary case in a naïve population. We modeled epidemic growth either from a single index case with illness onset on 8 December 2019 (Scenario 1), or using the growth rate fitted along with the other parameters (Scenario 2) based on data from 20 exported cases reported by 24 January 2020. The cumulative incidence in China by 24 January was estimated at 6924 cases (95% confidence interval [CI]: 4885, 9211) and 19,289 cases (95% CI: 10,901, 30,158), respectively. The latest estimated values of the cCFR were 5.3% (95% CI: 3.5%, 7.5%) for Scenario 1 and 8.4% (95% CI: 5.3%, 12.3%) for Scenario 2. The basic reproduction number was estimated to be 2.1 (95% CI: 2.0, 2.2) and 3.2 (95% CI: 2.7, 3.7) for Scenarios 1 and 2, respectively. Based on these results, we argued that the current COVID-19 epidemic has a substantial potential for causing a pandemic. The proposed approach provides insights in early risk assessment using publicly available data.