Long-Term Surveillance Data and Patterns of Invasion by Aedes albopictus in Florida

2008 ◽  
Vol 24 (1) ◽  
pp. 115-120 ◽  
Author(s):  
Seth C. Britch ◽  
Kenneth J. Linthicum ◽  
Assaf Anyamba ◽  
Compton J. Tucker ◽  
Edwin W. Pak
Keyword(s):  
Aedes Albopictus ◽  
Surveillance Data

DIPHTHERIA IN THE UNITED STATES, 1959-1960

PEDIATRICS ◽  
1962 ◽  
Vol 30 (2) ◽  
pp. 194-205
Author(s):  
Theodore C. Doege ◽  
Clark W. Heath ◽  
Ida L. Sherman
Keyword(s):  
United States ◽  
Communicable Disease ◽  
Case Fatality ◽  
The United States ◽  
Surveillance Data ◽  
Term Trend ◽  
The Past ◽  
Primary Series ◽  
Long Term Trend

Diphtheria attack rates and cases, and to a much lesser extent case-fatality rates, have fallen steadily within the United States during the past 25 years. However, during 1959 and 1960 there was a halt in this long-term trend. Epidemiologic data on 868 clinical cases of diphtheria occurring in 1959 and 873 cases in 1960 were submitted to the Communicable Disease Center by 45 states. The cases and several major outbreaks tended to concentrate in the southern and southwestern states. Attack rates and deaths were highest for children under 10 years, and attack rates were more than five times greater for nonwhite children. Analysis of 1960 immunization data shows that 72% of the patients had received no immunizations. Fifty-five per cent of carriers, but only 18% of persons with bacteriologically confirmed cases, had received a primary series. Only 1 person of 58 fatal cases occurring in 1960 had received a primary series. Certain problems for future investigation, disclosed by the surveillance data, are discussed.

scholarly journals Localized Surveillance: A Fresh Perspective for Regional Syndromic Surveillance

2015 ◽  
Vol 7 (1) ◽  
Author(s):  
Mansi Agarwal ◽  
Nimi Idaikkadar ◽  
José Lojo ◽  
Kristen Soto ◽  
Robert Mathes
Keyword(s):  
Public Health ◽  
Data Sharing ◽  
Syndromic Surveillance ◽  
Local Scale ◽  
Surveillance Data ◽  
Fresh Perspective

This roundtable will discuss successful syndromic surveillance data sharing efforts that have been used on a local scale for faster, more efficient, and long-term collaboration between neighboring public health jurisdictions.

scholarly journals Assessing scarlet fever re-emergence from notifiable disease surveillance in Hong Kong

2018 ◽  
Vol 10 (1) ◽  
Author(s):  
Eric H Lau ◽  
C Lee ◽  
B J Cowling
Keyword(s):  
Hong Kong ◽  
High Risk ◽  
Control Strategy ◽  
Scarlet Fever ◽  
Disease Surveillance ◽  
Surveillance Data ◽  
Notifiable Disease ◽  
Term Trend ◽  
Long Term Trend

Objective: This study examined the epidemiology of scarlet fever in Hong Kong based on notifiable disease surveillance data, in a period where a 10-fold upsurge in scarlet fever incidence occurred. High risk groups and important factors associated with scarlet fever transmission were identified.Introduction: Scarlet fever is a notifiable disease in Hong Kong for over 40 years. There was relatively low activity of scarlet fever until an outbreak in mid-2011 which resulted in two deaths and more than 1,500 cases. Scarlet fever incidence remained elevated since then with >10-fold increase comparing to that before the upsurge (1, 2). Reemergence of scarlet fever was also reported in China in 2011 and the United Kingdom in 2014 (3). We analyzed the patterns in scarlet fever incidence in Hong Kong using the notifiable disease surveillance data from 2005–2015.Methods: We analyzed 7,266 scarlet fever cases aged 14y or younger from 2005-2015, who were notified to the Department of Health. Hierarchical multivariable negative binomial models were fitted to the data to study the effects of age, sex, school holidays, and other meteorological parameters, accounting for autocorrelation, seasonal and long-term trend. Separate models were fitted to the data before and after the upsurge in 2011, excluding data in 2011 to allow for a 1-year window period.Results: We observed seasonal pattern throughout the study period (Figure). Among children aged ≤5y, the average scarlet fever incidence was 3.3 per 10,000 children in 2005-2010, which increased substantially to 18.1 per 10,000 children in 2012-2015.The final model included age, sex, school holidays in the preceding week, temperature, relative humidity, rainfall, long-term and bimodal seasonal trend. Based on the model, we identified no significant long-term trend before the upsurge in 2011, but there was a mild decreasing trend of about 8% (95% CI=6-11%) per year after the upsurge. A major peak was identified in December to January, with a milder peak in May to June.We found that the most affected groups were kindergarten students (3-5y), followed by primary school students (6-11y). Comparing to girls aged 0-2y, boys had significantly higher risk than girls except for the 0-2y age group, and boys aged 3-5y had the highest risk (adjusted incidence rate ratio (IRR)=1.47, 95% CI=1.32-1.65). School holidays were significantly associated with lower incidence of scarlet fever, with an adjusted IRR of 0.58 (95% CI=0.51–0.65) after the upsurge in 2011. Temperature was found to be negatively associated with scarlet fever incidence (adjusted IRR=0.963, 95% CI=0.940-0.987) after the upsurge.Conclusions: Our study showed that elevated activity of scarlet fever was sustained for more than 5 years after the upsurge in 2011. We found that younger children who started schools, especially for boys aged 3-5 years, had a higher risk of scarlet fever, and there was significant effect of school holidays in reducing scarlet fever incidence. Combining these findings, school-based control strategy is likely to be effective. Sustained and consistent surveillance of scarlet fever allows continued monitoring of potential change in high risk group to drive updated and effective control strategy.

scholarly journals From a Long-distance Threat to the Invasion Front: Invasive Aedes Mosquito Species in Belgium Between 2007 and 2020

2021 ◽  
Author(s):  
Isra Deblauwe ◽  
Katrien De Wolf ◽  
Jacobus De Witte ◽  
Anna Schneider ◽  
Ingrid Verlé ◽  
...  
Keyword(s):  
Aedes Albopictus ◽  
Mosquito Species ◽  
Aedes Mosquito ◽  
Long Distance ◽  
Invasion Front ◽  
Management Actions ◽  
Invasive Mosquito ◽  
And Control ◽  
Control Management

Abstract Background: Invasive mosquito species (IMS) and their associated mosquito-borne diseases are emerging in Europe. In Belgium, the first detection of Aedes albopictus occurred in 2000 and of Aedes japonicus in 2002. Early detection and control of these IMS at points of entry (PoE’s) are of paramount importance to slow down any possible establishment. This paper gives an account of the IMS surveillance in Belgium between 2007 and 2020 and reviews the introductions and establishments recorded in that period.Methods: In total 52 PoE’s were monitored at least once for the presence of IMS between 2007 and 2020. These included used tyre and lucky bamboo import companies, airports, ports, parking lots along highways, shelters for imported cutting plants, wholesale markets, industrial areas, recycling areas, and cemeteries and an allotment garden at the country border with colonised areas. In general, monitoring was performed between April and November. Mosquitoes were captured with adult and oviposition traps, as well as by larval sampling. A logistic regression was performed to investigate the percentage of positive PoE’s for Ae. albopictus over the years. Results: Aedes albopictus has been detected at ten PoE’s, Ae. japonicus at three PoE’s and Aedes koreicus at two PoE’s. The latter two species have established overwintering populations. The percentage of PoE’s positive for Ae. albopictus increased significantly over time. Aedes albopictus is currently entering Belgium through lucky bamboo trade, used tyre trade and passive ground transport, while Ae. japonicus only through the latter two pathways. In Belgium, the import through passive ground transport was first recorded in 2018 and its importance seems to grow.Conclusion: Belgium is currently at the invasion front of Ae. albopictus and Ae. japonicus. The surveillance and control management actions at well-known PoE‘s associated to long-distance introductions are more straightforward than at less-defined PoE’s associated with short-distance introductions from colonised areas. These latter PoE’s represent a new challenge for IMS management in Belgium in the coming years and stresses the urgence of implementing a sustainable, structured and long-term IMS management programme, integrating active and passive surveillance and control.

scholarly journals Estimating COVID-19 Hospitalizations in the United States with surveillance data using a Bayesian Hierarchical model

2021 ◽  
Author(s):  
Alexia Couture ◽  
Danielle Iuliano ◽  
Howard H Chang ◽  
Neha N Patel ◽  
Matthew Gilmer ◽  
...  
Keyword(s):  
United States ◽  
Hierarchical Model ◽  
Cumulative Incidence ◽  
Bayesian Hierarchical Model ◽  
The United States ◽  
Surveillance Data ◽  
Age Group ◽  
Covariate Selection ◽  
Bayesian Hierarchical

Introduction: In the United States, COVID-19 is a nationally notifiable disease, cases and hospitalizations are reported to the CDC by states. Identifying and reporting every case from every facility in the United States may not be feasible in the long term. Creating sustainable methods for estimating burden of COVID-19 from established sentinel surveillance systems is becoming more important. We aimed to provide a method leveraging surveillance data to create a long-term solution to estimate monthly rates of hospitalizations for COVID-19. Methods: We estimated monthly hospitalization rates for COVID-19 from May 2020 through April 2021 for the 50 states using surveillance data from COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) and a Bayesian hierarchical model for extrapolation. We created a model for six age groups (0-17, 18-49, 50-64, 65-74, 75-84, and ≥85 years), separately. We identified covariates from multiple data sources that varied by age, state, and/or month, and performed covariate selection for each age group based on two methods, Least Absolute Shrinkage and Selection Operator (LASSO) and Spike and Slab selection methods. We validated our method by checking sensitivity of model estimates to covariate selection and model extrapolation as well as comparing our results to external data. Results: We estimated 3,569,500 (90% Credible Interval:3,238,000 - 3,934,700) hospitalizations for a cumulative incidence of 1,089.8 (988.6 - 1,201.3) hospitalizations per 100,000 population with COVID-19 in the United States from May 2020 through April 2021. Cumulative incidence varied from 352 - 1,821per 100,000 between states. The age group with the highest cumulative incidence was aged greater than or equal to 85 years (5,583.1; 5,061.0 - 6,157.5). The monthly hospitalization rate was highest in December (183.8; 154.5 - 218.0). Our monthly estimates by state showed variations in magnitudes of peak rates, number of peaks and timing of peaks between states. Conclusions: Our novel approach to estimate COVID-19 hospitalizations has potential to provide sustainable estimates for monitoring COVID-19 burden, as well as a flexible framework leveraging surveillance data.

Sign in / Sign up

Export Citation Format

Share Document