Estimation of Instant Fatality Rate of COVID-19 in Wuhan and Hubei Based on Daily Case Notification Data

2020 ◽  
Author(s):  
Lei Cao ◽  
Ting-ting Huang ◽  
Jun-xia Zhang ◽  
Qi Qin ◽  
Si-yu Liu ◽  
...  
Keyword(s):  
Fatality Rate ◽  
Notification Data ◽  
Case Notification

scholarly journals A new method to calculate the instant case fatality rate of COVID-19 in Wuhan and Hubei of China

2020 ◽  
Author(s):  
Lei Cao ◽  
Ting-ting Huang ◽  
Jun-xia Zhang ◽  
Qi Qin ◽  
Si-yu Liu ◽  
...  
Keyword(s):  
Case Fatality Rate ◽  
Large Deviation ◽  
Case Fatality ◽  
Mortality Rates ◽  
Fatality Rate ◽  
Cure Rate ◽  
Wuhan City ◽  
Notification Data ◽  
And Control ◽  
Case Notification

Abstract The worst-hit area of coronavirus disease 2019 (COVID-19) in China was Wuhan City and its affiliated Hubei Province, where the outbreak has been well controlled. The case fatality rate (CFR) is the most direct indicator to evaluate the hazards of an infectious disease. However, most reported CFR on COVID-19 represent a large deviation from reality. We aimed to establish a more accurate way to estimate the CFR of COVID-19 in Wuhan and Hubei and compare it to the reality. The daily case notification data of COVID-19 from December 8, 2019, to May 1, 2020, in Wuhan and Hubei were collected from the bulletin of the Chinese authorities. The instant CFR of COVID-19 was calculated from the numbers of deaths and the number of cured cases, the two numbers occurred on the same estimated diagnosis dates. The instant CFR of COVID-19 was 1.3%-9.4% in Wuhan and 1.2%-7.4% in Hubei from January 1 to May 1, 2020. It has stabilized at 7.69% in Wuhan and 6.62% in Hubei since early April. The cure rate was between 90.1% and 98.8% and finally stabilized at 92.3% in Wuhan and stabilized at 93.5% in Hubei. The mortality rates were 34.5/100 000 in Wuhan and 7.61/100 000 in Hubei. In conclusion, this approach reveals a way to accurately calculate the CFR, which may provide a basis for the prevention and control of infectious diseases.

scholarly journals Estimation of instant case fatality rate of COVID-19 in Wuhan and Hubei based on daily case notification data

2020 ◽  
Author(s):  
Lei Cao ◽  
Ting-ting Huang ◽  
Jun-xia Zhang ◽  
Qi Qin ◽  
Si-yu Liu ◽  
...  
Keyword(s):  
Case Fatality Rate ◽  
Case Fatality ◽  
Hubei Province ◽  
Fatality Rate ◽  
Cure Rate ◽  
Wuhan City ◽  
Cure Time ◽  
Notification Data ◽  
Case Notification ◽  
Instant Case

AbstractBackgroundThe outbreak of coronavirus disease 2019 (COVID-19) initially appeared and has most rapidly spread in Wuhan, China. The case fatality rate is the most direct indicator to assess the hazards of an infectious disease. We aimed to estimate the instant fatality rate and cure rate of COVID-19 in Wuhan City and its affiliated Hubei Province.MethodsWe collected the daily case notification data of COVID-19 from Dec 8, 2019 to Mar 10, 2020 in Wuhan City and Hubei Province officially announced by the Chinese authority. The numbers of daily confirmed/deaths/cured cases and the numbers of daily cumulative confirmed/deaths/cured cases were obtained. The death time and cure time of COVID-19 patients were calculated based on the dates of diagnosis, death and discharge of individual cases. Then the estimated diagnosis dates of deaths and cured cases were obtained on the basis of the median death or medium cure time, respectively. Finally, the instant fatality rate of COVID-19 was calculated according to the numbers of deaths and cured cases on the same estimated diagnosis dates.ResultsFrom Jan 1, 2020 to Feb 22, 2020 in Wuhan City, the instant case fatality rate of COVID-19 was 3.4%∼19.5% and the instant cured rate was 80.0%∼96.6%. The average fatality rate reached 11.4% while the average cure rate was 88.6%. During the same period in Hubei Province, the instant case fatality rate was 3.8%∼16.6% and the instant cured rate was 83.4%∼96.6%. The average fatality rate and the average cure rate were 9.2% and 91.8%, respectively.ConclusionsThe fatality rate and cure rate of COVID-19 in Wuhan City and Hubei Province were underestimated. Wuhan showed higher fatality rate and cure rate than the whole Hubei Province did.

#59: Children as Sentinels for Active Transmission of Tuberculosis: Disease Mapping Modeling of Programmatic Data in a High-Burden Setting

2021 ◽  
Vol 10 (Supplement_1) ◽  
pp. S16-S17
Author(s):  
Gunasekera Kenneth ◽  
Warren Joshua ◽  
Cohen Ted
Keyword(s):  
Census Data ◽  
Disease Mapping ◽  
Population Data ◽  
Grid Cells ◽  
High Burden ◽  
Individual Level ◽  
Tuberculosis Transmission ◽  
Subsequent Investigation ◽  
Notification Data ◽  
Case Notification

Abstract Background To meet the transmission reduction goals of the End TB strategy, there is a growing interest in identifying and targeting case-finding efforts to tuberculosis“hotspots,” geographic regions of active transmission. Collecting and interpreting spatial and pathogenic genetic information, the most reliable evidence of active transmission, is prohibitively resource-intensive under routine conditions in high-burden settings. Many countries maintain case-notification registers under routine conditions, representing an attractive source of data to investigate for transmission. However, notification data are imperfect. Areas of high incidence may reflect other underlying patterns, and individual-level covariate information and other information that may aid in its interpretation, such as baseline census data or other healthcare utilization data, is often unavailable. Despite imperfections, the accessibility of notification data demands further investigation. We examined notification data from 2005 to 2007 in a South American, high-burden setting where the household address of each case was geocoded. Subsequent investigation of notification data in the same setting from 2009 to 2012 additionally provided pathogen genetic evidence from all culture-positive cases suggesting regions of active transmission of tuberculosis. We investigated a disease mapping modeling approach leveraging only age-specified tuberculosis notification data to suggest hotspots of active tuberculosis transmission. Methods Given the absence of baseline population data at a comparable spatial resolution, we aggregated the point-referenced cases reported to the Peruvian National Tuberculosis Program from 2005 to 2007 within two of Lima’s four health districts into a grid with 400 m × 400 m cells. We used Bayesian hierarchical spatial modeling methodology to model the proportion of children cases of the total number of adult and child cases in each cell. Where the modeled proportion of child cases is higher than expected, we suggest that case notification is driven primarily by active transmission. Results This method identified several grid cells in which the proportion of child cases is higher than expected. The location of these grid cells was found to approximate the location of active transmission evidenced by a later genotyping study. Conclusions This evidence suggests that age-specified notification data, with all its limitations, may be sufficient to suggest hotspots of active transmission of tuberculosis. We additionally provide the first spatial evidence to support the long-cited belief that with respect to tuberculosis transmission, childhood cases may truly be “the canary in the coal mine.”

scholarly journals Statistical Estimation of the Reproductive Number From Case Notification Data

2020 ◽  
Author(s):  
Laura F White ◽  
Carlee B Moser ◽  
Robin N Thompson ◽  
Marcello Pagano
Keyword(s):  
Statistical Estimation ◽  
Reproduction Number ◽  
Large Body ◽  
Real Data ◽  
Reproductive Number ◽  
Tremendous Progress ◽  
Notification Data ◽  
Infectious Disease Dynamics ◽  
Case Notification ◽  
Insight Into

Abstract The reproductive number, or reproduction number, is a valuable metric in understanding infectious disease dynamics. There is a large body of literature related to its use and estimation. In the last 15 years, there has been tremendous progress in statistically estimating this number using case notification data. These approaches are appealing because they are relevant in an ongoing outbreak (e.g., for assessing the effectiveness of interventions) and do not require substantial modelling expertise to be implemented. In this review, we describe these methods and the extensions that have been developed. We provide insight into the distinct interpretations of the estimators proposed and provide real data examples to illustrate how they are implemented. Finally we conclude with a discussion of available software and opportunities for future development.

scholarly journals Seventy-five years of estimating the force of infection from current status data

2009 ◽  
Vol 138 (6) ◽  
pp. 802-812 ◽  
Author(s):  
N. HENS ◽  
M. AERTS ◽  
C. FAES ◽  
Z. SHKEDY ◽  
O. LEJEUNE ◽  
...  
Keyword(s):  
Infectious Disease ◽  
Disease Burden ◽  
Parvovirus B19 ◽  
Current Status Data ◽  
Current Status ◽  
Force Of Infection ◽  
Status Data ◽  
Notification Data ◽  
Case Notification ◽  
Key Parameter

SUMMARYThe force of infection, describing the rate at which a susceptible person acquires an infection, is a key parameter in models estimating the infectious disease burden, and the effectiveness and cost-effectiveness of infectious disease prevention. Since Muench formulated the first catalytic model to estimate the force of infection from current status data in 1934, exactly 75 years ago, several authors addressed the estimation of this parameter by more advanced statistical methods, while applying these to seroprevalence and reported incidence/case notification data. In this paper we present an historical overview, discussing the relevance of Muench's work, and we explain the wide array of newer methods with illustrations on pre-vaccination serological survey data of two airborne infections: rubella and parvovirus B19. We also provide guidance on deciding which method(s) to apply to estimate the force of infection, given a particular set of data.

Modelling the impact of varicella vaccination on varicella and zoster

2009 ◽  
Vol 138 (4) ◽  
pp. 469-481 ◽  
Author(s):  
M. KARHUNEN ◽  
T. LEINO ◽  
H. SALO ◽  
I. DAVIDKIN ◽  
T. KILPI ◽  
...  
Keyword(s):  
Large Scale ◽  
Vaccine Coverage ◽  
Varicella Vaccine ◽  
Age Groups ◽  
Varicella Vaccination ◽  
Negative Consequences ◽  
Serological Data ◽  
Notification Data ◽  
The Impact ◽  
Case Notification

SUMMARYIt has been suggested that the incidence of herpes zoster may increase due to lack of natural boosting under large-scale vaccination with the varicella vaccine. To study the possibility and magnitude of such negative consequences of mass vaccination, we built a mathematical model of varicella and zoster epidemiology in the Finnish population. The model was based on serological data on varicella infection, case-notification data on zoster, and new knowledge about close contacts relevant to transmission of infection. According to the analysis, a childhood programme against varicella will increase the incidence of zoster by one to more than two thirds in the next 50 years. This will be due to increase in case numbers in the ⩾35 years age groups. However, high vaccine coverage and a two-dose programme will be very effective in stopping varicella transmission in the population.

scholarly journals Tuberculosis case notification data in Viet Nam, 2007 to 2012

2015 ◽  
Vol 6 (1) ◽  
pp. 7-14 ◽  
Author(s):  
Viet Nhung Nguyen ◽  
Binh Hoa Nguyen ◽  
Huyen Khanh Pham ◽  
Cornelia Hennig
Keyword(s):  
Viet Nam ◽  
Tuberculosis Case ◽  
Notification Data ◽  
Case Notification

scholarly journals Bayesian Modeling of Spatiotemporal patterns of TB-HIV co-infection risk in Kenya

2019 ◽  
Author(s):  
Verrah A Otiende ◽  
Thomas N O Achia ◽  
Henry G Mwambi
Keyword(s):  
Resource Allocation ◽  
Control Program ◽  
Infection Risk ◽  
Surveillance Systems ◽  
Hiv Status ◽  
Chi Square ◽  
Integration Of Care ◽  
The Government ◽  
Notification Data ◽  
Case Notification

Abstract Background TB and HIV diseases are globally acknowledged as a public health challenge that exhibits adverse bidirectional relations due to the co-epidemic overlap. To understand the co-infection burden we use the case notification data to generate spatiotemporal maps that describe new distribution and exposure hypotheses for further epidemiologic investigations in areas with unusual case notification levels. These model maps are important in deciding relevant geographically targeting interventions and resource allocation for suppressing co-infection Methods We did an extensive analysis of the TB and TB-HIV case notification data from the Kenya national TB control program. We analyzed the case notification data aggregated for forty-seven counties over a seven-year period (2012 – 2018). We assessed the geographic patterns by mapping the cumulative co-infection incidence rate in each county. We performed the chi-square tests to determine the association between HIV status and risk factors; TB-type, age, gender, and patient type. We stratified the data by HIV status. Using the Integrated Nested Laplace Approach (INLA), we modeled the risk of TB-HIV co-infection Results Of the total 608312 TB case notifications, 194129 were HIV co-infected. Over the period, the co-infection temporal risk trend was consistently higher in women as compared to men with patients aged below 25 years and above 54 years registering a considerably lower risk trend of TB-HIV co-infection. The spatial pattern of co-infection risk was widespread in males compared to the female. The counties with high co-infection burden for both male and female were Homabay, Siaya, Kisumu, Migori and Busia counties Conclusions TB-HIV co-epidemic in Kenya is at a critical point portending a dual endemic challenge for many years to come. The government of Kenya needs to combine surveillance systems for the TB and HIV National programs to optimize the TB-HIV coinfection case notification processes at all levels. Integration of care for both TB and HIV using a single facility and single health provider will enable proper monitoring of the co-infection trends, which will ensure adequate resource allocation to cause a significant impact in the reduction of HIV burden amongst TB patients and TB burden amongst HIV patients

scholarly journals Bayesian Modeling of Spatiotemporal patterns of TB-HIV co-infection risk in Kenya

2019 ◽  
Author(s):  
Verrah A Otiende ◽  
Thomas N O Achia ◽  
Henry G Mwambi
Keyword(s):  
Relative Risk ◽  
Temporal Trends ◽  
Control Program ◽  
Credible Interval ◽  
Infection Risk ◽  
Geographical Variations ◽  
Hiv Coinfection ◽  
Notification Data ◽  
Best Fit ◽  
Case Notification

Abstract Background Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) diseases are globally acknowledged as a public health challenge that exhibits adverse bidirectional relations due to the co-epidemic overlap. To understand the co-infection burden we used the case notification data to generate spatiotemporal maps that described the distribution and exposure hypotheses for further epidemiologic investigations in areas with unusual case notification levels. Methods We analyzed the TB and TB-HIV case notification data from the Kenya national TB control program aggregated for forty-seven counties over a seven-year period (2012 – 2018). Using the Integrated Nested Laplace Approach (INLA), we modeled the risk of TB-HIV co-infection. Six competing models with varying space-time formulations were compared to determine the best fit model. We then assessed the geographic patterns and temporal trends of coinfection risk by mapping the posterior marginal from the best fit model. Results Of the total 608312 TB case notifications, 194129 were HIV co-infected. The proportion of TB-HIV co-infection was higher in females (39.7%) than in males (27.0%). A significant share of the co-infection was among adults aged 35 to 44 years (46.7%) and 45 to 54 years (42.1%). Based on the Bayesian Defiance Information (DIC) and the effective number of parameters comparisons, the spatiotemporal model 3b was the best in explaining the geographical variations in TB-HIV coinfection. The model results suggested that the risk of TB-HIV coinfection was influenced by infrastructure index (Relative risk (RR)=5.75, Credible Interval (Cr.I) = (1.65, 19.89)) and gender ratio . The lowest and highest temporal relative risks were in the years 2016 at 0.9 and 2012 at 1.07 respectively. The spatial pattern presented an increased co-infection risk in a number of counties. For the spatiotemporal interaction, only a few counties had a relative risk greater than 1 that varied in different years. Conclusions We identified elevated risk areas for TB/HIV co-infection and fluctuating temporal trends which could be because of improved TB case detection or surveillance bias caused by spatial heterogeneity in the co-infection dynamics. Focused interventions and continuous TB-HIV surveillance will ensure adequate resource allocation and significant reduction of HIV burden amongst TB patients

scholarly journals Estimating Dengue Transmission Intensity from Case-Notification Data from Multiple Countries

2016 ◽  
Vol 10 (7) ◽  
pp. e0004833 ◽  
Author(s):  
Natsuko Imai ◽  
Ilaria Dorigatti ◽  
Simon Cauchemez ◽  
Neil M. Ferguson
Keyword(s):  
Transmission Intensity ◽  
Dengue Transmission ◽  
Notification Data ◽  
Case Notification
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