Clinical and Epidemiological Aspects of Diphtheria: A Systematic Review and Pooled Analysis

Abstract Background Diphtheria, once a major cause of childhood morbidity and mortality, all but disappeared following introduction of diphtheria vaccine. Recent outbreaks highlight the risk diphtheria poses when civil unrest interrupts vaccination and healthcare access. Lack of interest over the last century resulted in knowledge gaps about diphtheria’s epidemiology, transmission, and control. Methods We conducted 9 distinct systematic reviews on PubMed and Scopus (March–May 2018). We pooled and analyzed extracted data to fill in these key knowledge gaps. Results We identified 6934 articles, reviewed 781 full texts, and included 266. From this, we estimate that the median incubation period is 1.4 days. On average, untreated cases are colonized for 18.5 days (95% credible interval [CrI], 17.7–19.4 days), and 95% clear Corynebacterium diphtheriae within 48 days (95% CrI, 46–51 days). Asymptomatic carriers cause 76% (95% confidence interval, 59%–87%) fewer cases over the course of infection than symptomatic cases. The basic reproductive number is 1.7–4.3. Receipt of 3 doses of diphtheria toxoid vaccine is 87% (95% CrI, 68%–97%) effective against symptomatic disease and reduces transmission by 60% (95% CrI, 51%–68%). Vaccinated individuals can become colonized and transmit; consequently, vaccination alone can only interrupt transmission in 28% of outbreak settings, making isolation and antibiotics essential. While antibiotics reduce the duration of infection, they must be paired with diphtheria antitoxin to limit morbidity. Conclusions Appropriate tools to confront diphtheria exist; however, accurate understanding of the unique characteristics is crucial and lifesaving treatments must be made widely available. This comprehensive update provides clinical and public health guidance for diphtheria-specific preparedness and response.

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Estimation of the Outbreak Severity and Evaluation of Epidemic Prevention Ability of COVID-19 by Province in China

American Journal of Public Health ◽

10.2105/ajph.2020.305893 ◽

2020 ◽

Vol 110 (12) ◽

pp. 1837-1843

Author(s):

Yilei Ma ◽

Xuehan Liu ◽

Weiwei Tao ◽

Yuchen Tian ◽

Yanran Duan ◽

...

Keyword(s):

Emergency Response ◽

Disease Transmission ◽

Initial Period ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Linear Mixed Effects Model ◽

Linear Mixed Effects ◽

Imported Cases ◽

And Control ◽

Case Data

Objectives. To compare the epidemic prevention ability of COVID-19 of each province in China and to evaluate the existing prevention and control capacity of each province. Methods. We established a quasi-Poisson linear mixed-effects model using the case data in cities outside Wuhan in Hubei Province, China. We adapted this model to estimate the number of potential cases in Wuhan and obtained epidemiological parameters. We estimated the initial number of cases in each province by using passenger flowrate data and constructed the extended susceptible–exposed–infectious–recovered model to predict the future disease transmission trends. Results. The estimated potential cases in Wuhan were about 3 times the reported cases. The basic reproductive number was 3.30 during the initial outbreak. Provinces with more estimated imported cases than reported cases were those in the surrounding provinces of Hubei, including Henan and Shaanxi. The regions where the number of reported cases was closer to the predicted value were most the developed areas, including Beijing and Shanghai. Conclusions. The number of confirmed cases in Wuhan was underestimated in the initial period of the outbreak. Provincial surveillance and emergency response capabilities vary across the country.

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The Bayesian Susceptible-Exposed-Infected-Recovered model for the outbreak of COVID-19 on the Diamond Princess Cruise Ship

Stochastic Environmental Research and Risk Assessment ◽

10.1007/s00477-020-01968-w ◽

2021 ◽

Author(s):

Chao-Chih Lai ◽

Chen-Yang Hsu ◽

Hsiao-Hsuan Jen ◽

Amy Ming-Fang Yen ◽

Chang-Chuan Chan ◽

...

Keyword(s):

Estimation Method ◽

Credible Interval ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Cruise Ship ◽

Seir Model ◽

Mcmc Estimation ◽

Epidemic Period ◽

Containment Measures ◽

Containment Measure

AbstractThe outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R0) and the effectiveness of containment measures. We developed an ordinary differential equation-based Susceptible-Exposed-Infected-Recovery (SEIR) model with Bayesian underpinning to estimate the main parameter of R0 determined by transmission coefficients, incubation period, and the recovery rate. Bayesian Markov Chain Monte Carlo (MCMC) estimation method was used to tackle the parameters of uncertainty resulting from the outbreak of COVID-19 given a small cohort of the cruise ship. The extended stratified SEIR model was also proposed to elucidate the heterogeneity of transmission route by the level of deck with passengers and crews. With the application of the overall model, R0 was estimated as high as 5.70 (95% credible interval: 4.23–7.79). The entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 63% (95% credible interval: 60–66%) infected passengers. With the deck-specific SEIR model, the heterogeneity of R0 estimates by each deck was noted. The estimated R0 figures were 5.18 for passengers (5–14 deck), mainly from the within-deck transmission, and 2.46 for crews (2–4 deck), mainly from the between-deck transmission. Modelling the dynamic of COVID-19 on the cruise ship not only provides an insight into timely evacuation and early isolation and quarantine but also elucidates the relative contributions of different transmission modes on the cruise ship though the deck-stratified SEIR model.

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Bayesian approach for modelling the dynamic of COVID-19 outbreak on the Diamond Princess Cruise Ship

10.1101/2020.06.21.20136465 ◽

2020 ◽

Author(s):

Chao-Chih Lai ◽

Chen-Yang Hsu ◽

Hsiao-Hsuan Jen ◽

Amy Ming-Fang Yen ◽

Chang-Chuan Chan ◽

...

Keyword(s):

Population Size ◽

Large Population ◽

Credible Interval ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Cruise Ship ◽

Epidemic Curve ◽

Cruise Ships ◽

Epidemic Period ◽

Containment Measures

AbstractThe outbreaks of acute respiratory infectious disease with high attack rates on cruise ships were rarely studied. The outbreak of COVID-19 on the Diamond Princess Cruise Ship provides an unprecedented opportunity to estimate its original transmissibility with basic reproductive number (R0) and the effectiveness of containment measures. The traditional deterministic approach for estimating R0 is based on the outbreak of a large population size rather than that a small cohort of cruise ship. The parameters are therefore fraught with uncertainty. To tackle this problem, we developed a Bayesian Susceptible-Exposed-Infected-Recovery (SEIR) model with ordinary differential equation (ODE) to estimate three parameters, including transmission coefficients, the latent period, and the recovery rate given the uncertainty implicated the outbreak of COVID-19 on cruise ship with modest population size. Based on the estimated results on these three parameters before the introduction of partial containment measures, the natural epidemic curve after intervention was predicted and compared with the observed curve in order to assess the efficacy of containment measures. With the application of the Bayesian model to the empirical data on COVID-19 outbreak on the Diamond Princess Cruise Ship, the R0 was estimated as high as 5.71(95% credible interval: 4.08-7.55) because of its aerosols and fomite transmission mode. The simulated trajectory shows the entire epidemic period without containment measurements was approximately 47 days and reached the peak one month later after the index case. The partial containment measure reduced 34% (95% credible interval: 31-36%) infected passengers. Such a discovery provides an insight into timely evacuation and early isolation and quarantine with decontamination for containing other cruise ships and warship outbreaks.

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Macro-parasite transmission in dynamic seasonal environment: Basic Reproductive Number, endemicity, and control

10.1101/19012245 ◽

2019 ◽

Author(s):

Q. Huang ◽

D. Gurarie ◽

M. Ndeffo-Mbah ◽

E. Li ◽

CH. King

Keyword(s):

Intermediate Host ◽

Worm Burden ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Optimal Time ◽

Optimal Timing ◽

Snail Population ◽

Seasonal Environment ◽

Combined Strategy ◽

And Control

AbstractSeasonality of transmission environment, which includes snail populations and habitats, or human-snail contact patterns, can affect the dynamics of schistosomiasis infection, and control outcomes. Conventional modeling approaches often ignore or oversimplify it by applying ‘seasonal mean’ formulation. Mathematically, such ‘averaging’ is justified when model outputs/quantities of interest depend linearly on input variables. That is not generally the case for macroparasite transmission models, where model outputs are nonlinear functions of seasonality fashion.Another commonly used approach for Schistosomiasis modeling is a reduction of coupled human-snail system to a single ‘human equation’, via quasi-stationary snail (intermediate host) dynamics. The basic questions arising from these approaches are whether such ‘seasonal averaging’ and ‘intermediate host reduction’ are suitable for highly variable/seasonal environments, and what implications these methods have on models’ predictive potential of control interventions.Here we address these questions by using a combination of mathematical analysis and numerical simulation of two commonly used models for macroparasite transmission, MacDonald (MWB), and stratified worm burden (SWB) snail-human systems. We showed that predictions from ‘seasonal averaging’ models can depart significantly from those of quasi-stationary models. Typically, seasonality would lower endemicity and sustained infection, vs. stationary system with comparable transmission inputs. Furthermore, discrepancies between the two models (‘seasonal’ and its ‘stationary mean’) increase with amplitude (or variance) of seasonality. So sufficiently high variability can render infection unsustainable. Similar discrepancies were observed between coupled and reduced ‘single host’ models, with reduced model overpredicting sustained endemicity. Seasonal variability of transmission raises the question of optimal control timing. Using dynamic simulation, we show that optimal timing of repeated MDA is about half season past the snail peak, where snail population attains its minimal value. Compared to sub-optimal timing, such strategy can reduce human worm burden by factor 2 after 5-6 rounds of MDA. We also extended our models for dynamic snail populations, which allowed us to study the effect of repeated molluscicide, or combined strategy (MDA + molluscicide). The optimal time for molluscicide alone is the end or the start of season, and combined strategy can give additional reduction, and in some cases lead to elimination.Overall, reduced sustainability in seasonal environment makes it more amenable to control interventions, compared to stationary environment.

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Insight into Malaria Transmission and Control in Endemic Areas

Online Journal of Public Health Informatics ◽

10.5210/ojphi.v7i1.5954 ◽

2015 ◽

Vol 7 (1) ◽

Author(s):

Beatty V. Maikai ◽

Jarlath U. Umoh ◽

Victor A. Maikai

Keyword(s):

Malaria Control ◽

Control Program ◽

Basic Reproductive Number ◽

Reproductive Number ◽

The Status ◽

Health Concept ◽

Endemic Areas ◽

The One ◽

And Control ◽

State Regional

The global effort of malaria control is in line with the one world one health concept, but then a globally defined (one-size-fits-all) malaria control strategy would be inefficient. A model was used to examine the likely impact of malaria parasite interventions for a steady state regional control program in endemic areas. Assumptions varied about two targeted epidemiologic control points on the basic reproductive number, Ro, which is affected by different factors and upon which the status of malaria in any community will depend. For any effective malaria control and eradication program, environmental and socio-economic factors should also be considered.

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Characteristics and evaluation of the effectiveness of monitoring and control measures for the first 69 Patients with COVID-19 from 18 January 2020 to 2 March in Wuxi, China

10.21203/rs.3.rs-25895/v1 ◽

2020 ◽

Author(s):

Ping Shi ◽

Yumeng Gao ◽

Yuan Shen ◽

Enping Chen ◽

Hai Chen ◽

...

Keyword(s):

Risk Factors ◽

Close Contact ◽

Virus Detection ◽

Control Measures ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Monitoring And Control ◽

Infected People ◽

Novel Coronavirus ◽

And Control

Abstract Background: The novel coronavirus disease 2019(COVID-19) outbreak and has caused has caused 82,830 confirmed cases and 4,633 deaths in China by 26 April 2020. We analyzed data on 69 infections in Wuxi to describe the epidemiologic characteristics and evaluate the control measures.Methods: The demographic characteristics, exposure history, and illness timelines of COVID-19 cases in Wuxi were collected.Results: Among the 69 positive infections with COVID-19, mild and normal types accounted for 75.36% (52/69), adolescents and children are mainly mild and asymptomatic. The basic reproductive number was estimated to be 1.12 (95% CI, 0.71 to 1.69). The mean incubation period was estimated to be 4.77 days (95% CI, 3.61 to 5.94), with a mean serial interval of 6.31 days (95%CI, 5.12 to 7.50). We also found that age (RR=1.57, 95%CI: 1.11-2.21) and fever (RR=4.09, 95%CI: 1.10-15.19) were risk factors for COVID-19 disease severity.Conclusions: The incidence of COVID-19 in Wuxi has turned into a lower level, suggesting that the early prevention and control measures have achieved effectiveness. The community transmission can be effectively prevented through isolation and virus detection of all the people who were exposed together and close contact with the infected people. Aging and fever are risk factors for clinical outcome, which might be useful for preventing severe transition.

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Using proper mean generation intervals in modelling of COVID-19

10.1101/2021.03.25.21254307 ◽

2021 ◽

Author(s):

Xiujuan Tang ◽

Salihu S Musa ◽

Shi Zhao ◽

Daihai He

Keyword(s):

Time Lag ◽

Basic Reproductive Number ◽

Infectious Period ◽

Reproductive Number ◽

Secondary Case ◽

Primary Case ◽

Control Efficacy ◽

The Mean ◽

And Control ◽

Parameter Values

In susceptible-exposed-infectious-recovered (SEIR) epidemic models, with the exponentially distributed duration of exposed/infectious statuses, the mean generation interval (GI, time lag between infections of a primary case and its secondary case) equals the mean latent period (LP) plus the mean infectious period (IP). It was widely reported that the GI for COVID-19 is as short as 5 days. However, many works in top journals used longer LP or IP with the sum (i.e., GI), e.g., > 7 days. This discrepancy will lead to overestimated basic reproductive number, and exaggerated expectation of infectious attack rate and control efficacy, since all these quantities are functions of basic reproductive number. We argue that it is important to use suitable epidemiological parameter values.

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Evaluating new evidence in the early dynamics of the novel coronavirus COVID-19 outbreak in Wuhan, China with real time domestic traffic and potential asymptomatic transmissions

10.1101/2020.02.15.20023440 ◽

2020 ◽

Cited By ~ 12

Author(s):

Can Zhou

Keyword(s):

Real Time ◽

Early Stage ◽

Empirical Support ◽

Credible Interval ◽

Basic Reproductive Number ◽

Reproductive Number ◽

The Novel ◽

Sources Of Information ◽

Alternative Sources ◽

Novel Coronavirus

AbstractThe novel coronavirus (COVID-19), first detected in Wuhan, China in December 2019, has spread to 28 countries/regions with over 43,000 confirmed cases. Much about this outbreak is still unknown. At this early stage of the epidemic, it is important to investigate alternative sources of information to understand its dynamics and spread. With updated real time domestic traffic, this study aims to integrate recent evidence of international evacuees extracted from Wuhan between Jan. 29 and Feb. 2, 2020 to infer the dynamics of the COVD-19 outbreak in Wuhan. In addition, a modified SEIR model was used to evaluate the empirical support for the presence of asymptomatic transmissions. Based on the data examined, this study found little evidence for the presence of asymptomatic transmissions. However, it is still too early to rule out its presence conclusively due to sample size and other limitations. The updated basic reproductive number was found to be 2.12 on average with a 95% credible interval of [2.04, 2.18]. It is smaller than previous estimates probably because the new estimate factors in the social and non-pharmaceutical mitigation implemented in Wuhan through the evacuee dataset. Detailed predictions of infected individuals exported both domestically and internationally were produced. The estimated case confirmation rate has been low but has increased steadily to 23.37% on average. The findings of this study depend on the validity of the underlying assumptions, and continuing work is needed, especially in monitoring the current infection status of Wuhan residents.

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A scrapie epidemic in Cyprus

Epidemiology and Infection ◽

10.1017/s0950268804002110 ◽

2004 ◽

Vol 132 (4) ◽

pp. 751-760 ◽

Cited By ~ 10

Author(s):

M. B. GRAVENOR ◽

P. PAPASOZOMENOS ◽

A. R. McLEAN ◽

G. NEOPHYTOU

Keyword(s):

Early Identification ◽

Point Of View ◽

Basic Reproductive Number ◽

Reproductive Number ◽

Economic Point ◽

Long Period ◽

And Control ◽

The Impact ◽

Epidemiological Information ◽

Scarce Data

Scrapie is endemic in the sheep flocks of many countries, but good epidemiological information on this disease is scarce. Data on the initial stages of an epidemic are even more rare. We describe the ongoing epidemic of scrapie in Cyprus that has been tracked since it began in the mid-1980s. The early stages of the spread of scrapie from farm to farm, between 1985 and 2000, is analysed with a simple mathematical model. The flock-to-flock basic reproductive number (R0) for the spread of scrapie was estimated at between 1·4 and 1·8. The impact of interventions on the control of the epidemic are discussed from an epidemiological and economic point of view. Early identification of scrapie cases on farms can have a large impact on the number of farms affected. The long period before detection of disease in a flock means that policies based on whole-flock slaughter can be inefficient in preventing spread. Under a range of scenarios, a concentration of resources on early detection and quarantine may be more effective in terms of both the costs and control of the epidemic.

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