Chuang et al. BMC Infectious Diseases 2010, 10:136 http://www.biomedcentral.com/1471-2334/10/136 Open Access RESEARCH ARTICLE © 2010 Chuang et al; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Research article A dynamic estimation of the daily cumulative cases during infectious disease surveillance: application to dengue fever Pei-Hung Chuang 1,2 , Jen-Hsiang Chuang 1,2,4 and I-Feng Lin* 1,3 Abstract Background: In infectious disease surveillance, when the laboratory confirmation of the cases is time-consuming, there is often a time lag between the number of suspect cases and the number of confirmed cases. This study proposes a dynamic statistical model to estimate the daily number of new cases and the daily cumulative number of infected cases, which was then applied to historic dengue fever data. Methods: The duration between the date of disease onset and date of laboratory confirmation was assumed to follow a gamma distribution or a nonparametric distribution. A conditional probability of a case being a real case among the unconfirmed cases on a given date was then calculated. This probability along with the observed confirmed cases was integrated to estimate the daily number of new cases and the cumulative number of infected cases. Results: The distribution of the onset-to-confirmation time for the positive cases was different from that of the negative cases. The daily new cases and cumulative epidemic curves estimated by the proposed method have a lower absolute relative bias than the values estimated solely based on the available daily-confirmed cases. Conclusion: The proposed method provides a more accurate real-time estimation of the daily new cases and daily cumulative number of infected cases. The model makes use of the most recent "moving window" of information relative to suspect cases and dynamically updates the parameters. The proposed method will be useful for the real- time evaluation of a disease outbreak when case classification requires a time-consuming laboratory process to identify a confirmed case. Background Timeliness and accuracy of case reporting are two of the most important performance measures when evaluating an infectious disease surveillance system [1-5]. Labora- tory confirmation is usually needed for case diagnosis in many infectious diseases. When laboratory confirmation of the diagnosis is time-consuming, however, there is often a time-lag between the onset date of symptoms and the diagnosis date [6]. For example, the median time for confirmation of the meningococcal disease is about 13 days based on the National Notifiable Diseases Surveil- lance System (NNDSS) dataset for the United States from 1999 to 2001 [7]. Time from disease onset to diagnosis has been also reported to account for most of the delay in case reporting in Korea [8]. Correct estimation of daily cases or daily-cumulative infected cases helps the imple- ment of immediate disease control and allows prevention activities for infectious diseases to be instituted [6]. Using a disease surveillance system, one is able to apply statisti- cal methods, such as cumulative sum (CuSum) [9,10] or autoregressive integrated moving average (ARIMA) [11- 15], in order to forecast an epidemic curve or to detect aberrations in disease spread. These estimations are based on having a correct daily number of cases or a daily-cumulative number of cases. An epidemic of dengue fever occurs every year in southern Taiwan. In the year 2002 in particular, there was a large-scale epidemic with 5,388 confirmed cases out of totally 15,221 suspect cases [16]. This epidemic contin- ued until March 2003. Surveillance and the control of dengue fever have become one of the most important * Correspondence: iflin@ym.edu.tw 1 Institute of Public Health, School of Medicine, National Yang-Ming University, T aipei, Taiwan Full list of author information is available at the end of the article