Epidemiologic Analysis of Infectious Disease Data

Research in the field of infectious disease epidemiology has never been more relevant. Epidemiologic analyses can be applied to infectious disease datasets to assess early indicators of disease transmission and factors associated with infection. Novel strategies for population-based and syndromic surveillance, as well as new findings assessing the risk factors for infectious diseases, can be quickly implemented in the field of public health to minimize the risk of disease transmission. In Chapter 1, a novel syndromic surveillance platform (FluSense) for influenza-like illness (ILI) was evaluated for its ability to detect ILI outbreaks in four waiting areas of a university clinic. The facility-level model showed a significant association of coughs/person-day with influenza tests, indicating a 1% increase in testing per person-cough. The walk-in and women’s health rooms showed significant associations of sneezes/person-day with influenza tests. The FluSense platform can be used to passively detect ILI in an anonymous and confidential manner. In Chapter 2, serological surveys were conducted to estimate the cumulative incidence of prior SARS-CoV-2 infection within Massachusetts in summer 2020 and identify risk factors associated with infection. Estimated weighted seroprevalences were 5.3% (95% CI: 3.5 – 8.0) for the primary sampling group (undergraduate students) and 4.0% (95% CI: 2.2 – 7.4) for the secondary sampling group (employees). Male gender, American Indian/Alaska Native and Black race/ethnicity, self-reported febrile illness, and geographic region had statistically significant associations with seropositivity. The results provide information regarding levels of undiagnosed COVID-19 infections. In Chapter 3, the association between household socioeconomic status (SES) and malaria-specific knowledge, attitudes, and prevention practices (KAP) were evaluated in Lao People’s Democratic Republic. Linear and logistic regression models were performed separately for each outcome variable. Education showed a significant positive association with KAP for most education categories. Assets showed significant positive associations with KAP except for binary knowledge. As assets increased by one, the odds of more favorable attitudes towards malaria testing and treatment increased by 10%. Occupation did not show a significant association with KAP. This study can provide support for the implementation of programs that increase assets and education to obtain higher levels of malaria-specific KAP