Applications of Likelihood-based inference with non-mechanistic and mechanistic models in infectious disease modeling

Presented June 21, 2019.

In this talk, Dr. Daihai He presents his recent works on applications of likelihood-based inference with non-mechanistic and mechanistic models in infectious disease modeling. Examples include modeling of the transmission of influenza, measles, yellow-fever virus, Zika virus, and Lassa-fever virus. Combined non-mechanistic and mechanistic models, we gain new insight into the mechanisms under the transmission of infectious diseases. 

June 21, 2019

Anthrax Assist: Modeling Tool for Planning and Decision Support During Early Days of an Anthrax Event

Presented April 24, 2018.

*This article was selected as the second prize awardee of the 2018 ISDS Awards for Outstanding Research Articles in Biosurveillance in the category of "Scientific Achievement."

April 24, 2018

Modelling the transmission and control strategies of varicella in Shenzhen

Varicella (chickenpox) is a highly transmissible childhood disease. Between 2010 and 2015,it displayed two epidemic waves annually among school populations in Shenzhen, China. However, their transmission dynamics remain unclear and there is no school-based vaccination programme in Shenzhen to-date. In this study, we developed a mathematical model to compare a school-based vaccination intervention scenario with a baseline (i.e. no intervention)scenario.


January 25, 2018

Agent-based investigation of sexually transmitted infection

Every year nearly 12 million new cases of syphilis in the world are registered. Currently, in many countries of the world the stabilization or even reduction of the incidence of syphilis is marked, but this does not apply to Ukraine.

May 26, 2017

Facilitating the Use of Epidemiological Models for Infectious Disease Surveillance

Epidemiological modeling for infectious disease is useful for disease management and routine implementation needs to be facilitated through better description of models in an operational context. A standardized model characterization process that allows selection or making manual comparisons of available models and their results is currently lacking. Los Alamos National Laboratory (LANL) has developed a comprehensive framework that can be used to characterize an infectious disease model in an operational context.

June 19, 2017

Integrated surveillance: Joint modeling of rodent and human tularemia cases in Finland

An increasing number of geo-coded information streams are available with possible use in disease surveillance applications. In this setting, multivariate modeling of health and non-health data allows assessment of concurrent patterns among data streams and conditioning on one another. Therefore it is appropriate to consider the analysis of their spatial distributions together. Specifically for vector-borne diseases, knowledge of spatial and temporal patterns of vector distribution could inform incidence in humans.

July 07, 2017

Modeling spatial and temporal variability by Bayesian multilevel model

The early detection of outbreaks of diseases is one of the most challenging objectives of epidemiological surveillance systems. In order to achieve this goal, the primary foundation is using those big surveillance data for understanding and controlling the spatiotemporal variability of disease through populations. Typically, public health’s surveillance system would generate data with the big data characteristics of high volume, velocity, and variety.

July 16, 2017

Respiratory and circulatory deaths attributable to influenza A & B

Assigning causes of deaths to seasonal infectious diseases is difficult in part due to laboratory testing prior to death being uncommon. Since influenza (and other common respiratory pathogens) are therefore notoriously underreported as a (contributing) cause of death in deathcause statistics modeling studies are commonly used to estimate the impact of influenza on mortality.


To estimate mortality attributable to influenza adjusted for other common respiratory pathogens, baseline seasonal trends and extreme temperatures.

August 08, 2017

Semantic Analysis of Open Source Data for Syndromic Surveillance

Social media messages are often short, informal, and ungrammatical. They frequently involve text, images, audio, or video, which makes the identification of useful information difficult. This complexity reduces the efficacy of standard information extraction techniques1. However, recent advances in NLP, especially methods tailored to social media2, have shown promise in improving real-time PH surveillance and emergency response3. Surveillance data derived from semantic analysis combined with traditional surveillance processes has potential to improve event detection and characterization.

August 10, 2017

Multivariate Count Time Series Modeling of Surveillance Data

Surveillance data on various notifiable diseases usually consist of multiple time series of daily, weekly, or monthly counts of new infections. Data are typically reported in several strata defined through administrative geographical areas, gender and/or age groups. Statistical modeling of the resulting multivariate time series is an important task in infectious disease epidemiology. We will discuss time series models - specifically developed for multivariate surveillance count data - that can be used for two distinct roles, understanding and prediction of disease spread.

March 13, 2017


Contact Us

NSSP Community of Practice



This website is supported by Cooperative Agreement # 6NU38OT000297-02-01 Strengthening Public Health Systems and Services through National Partnerships to Improve and Protect the Nation's Health between the Centers for Disease Control and Prevention (CDC) and the Council of State and Territorial Epidemiologists. Its contents are solely the responsibility of the authors and do not necessarily represent the official views of CDC. CDC is not responsible for Section 508 compliance (accessibility) on private websites.

Site created by Fusani Applications