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Resources for Influenza Research

Note that the resources listed here can also be applied to general infectious respiratory diseases such as COVID-19.

Topics and Keywords

  • Influenza | Flu
  • Pandemic (Antigenic Shift) vs. Epidemic (Antigenic Drift)
  • Epidemiology of Influenza
  • Modeling Influenza
    • Transmission
    • Forecasting
  • Surveillence of Influenza
  • Ecology of Influenza
  • Evolution of Influenza

Infectious Disease Epidemiology

Textbooks

  • Gordis, L. (2013). Epidemiology. Elsevier: Saunders.
    • Chapter 2 The Dynamics of Disease Transmission
  • Nelson, K. E., & Williams, C. (2013). Infectious disease epidemiology. Jones & Bartlett Publishers.
    • Chapter 6 Infectious Disease Dynamics
    • Chapter 7 Geographic Information Systems
    • Chapter 15 Epidemiology and Prevention of Influenza

Modeling Infectious Diseases

Textbooks

Review Papers

Ecology and Evolution of Influenza

Textbook

  • Webster, R. G., Monto, A. S., Braciale, T. J., & Lamb, R. A. (2014). Textbook of Influenza. John Wiley & Sons.

Review Papers

Seasonality of Influenza

Key Paper

Review Papers

Dynamics of Influenza

Influenza Transmission

Classical Paper

Influenza Forecasting

Review Papers

Projects

Surveillance of Influenza

Review Paper

Digital Detection of Influenza

Review Paper

  • Salathe, M., Bengtsson, L., Bodnar, T. J., Brewer, D. D., Brownstein, J. S., Buckee, C., … & Vespignani, A. (2012). Digital Epidemiology. PLoS computational biology, 8(7), e1002616.

Classical Paper

Projects

  • Google Flu Trends: using aggregated Google search data to estimate flu activity.
  • HealthMap: flu & Ebola map | virus & contagious disease surveillence.

Databases for Influenza Research

Software Packages for Influenza Research

R Packages

  • cdcfluview: Retrieve U.S. Flu Season Data from the CDC FluView Portal.
  • coarseDataTools: Analysis of Coarsely Observed Data.
  • EpiDynamics: Dynamic Models in Epidemiology. Currently, the R package EpiDynamics implements the computer programs written in other programming languages and available in the web page of the book written by Keeling & Rohani (2008). Python Programs for this book can also be found here.
  • epidemics: An R package to define seasonal influenza epidemic onset and duration.
  • epimdr: Functions and Data for “Epidemics: Models and Data in R”.
  • EpiModel: Mathematical Modeling of Infectious Disease.
  • EpiNow2: Estimate Real-Time Case Counts and Time-Varying Epidemiological Parameters.
  • epinowcast: Flexible hierarchical nowcasting.
  • epitools: Tools for training and practicing epidemiologists including methods for two-way and multi-way contingency tables.
  • epinet: An R package to analyze epidemics spread across contact networks. Details are described in Groendyke & Welch (2018).
  • fitR: Provides functions for model fitting and inference.
  • mem: The Moving Epidemic Method, created by Tomás Vega and José E. Lozano (see details in Vega et al. (2013) and Vega et al. (2015)), allows the weekly assessment of the epidemic and intensity status to help in routine respiratory infections surveillance in health systems.
  • R0: Estimation of R0 and Real-Time Reproduction Number from Epidemics. Details are described in Obadia et al. (2012).
  • socialmixr: Provides methods for sampling contact matrices from diary data for use in infectious disease modelling, as discussed in Mossong et al. (2008).
  • tsiR: An implementation of the time-series Susceptible-Infected-Recovered (TSIR) model described by Finkenstädt & Grenfell (2000) using a number of different fitting options for infectious disease time series data.
  • tycho2: R interface to Project Tycho 2.0 API.
  • Projects of R Epidemics Consortium (RECON): lists released projects and packages, up-and-coming packages, and related packages that authored by RECON members and relevant for infectious disease epidemiology. The precursor of RECON is The R-epi project, which will eventually be replaced by the RECON website.

Python Packages

  • pypfilt: Bootstrap particle filter for epidemic forecasting.
  • epifx: Epidemic forecasting with mechanistic infection models.

Workshops and Conferences

Courses

Massive Open Online Courses (MOOCs)

  • Epidemics - the Dynamics of Infectious Diseases: a course provided by the Pennsylvania State University discusses about the dynamics of Malaria, HIV/AIDS, Influenza, Measles - how they emerge, how they spread around the globe, and how they can best be controlled. The R package epimdr is an advanced quantitative companion to this course.

  • Epidemics: a course provided by the University of Hong Kong covers these four topics: origins of novel pathogens; analysis of the spread of infectious diseases; medical and public health countermeasures to prevent and control epidemics; panel discussions involving leading public health experts with deep frontline experiences to share their views on risk communication, crisis management, ethics and public trust in the context of infectious disease control.

Short courses

Summer School

Channels

  • Disease Forecasting & Surveillance: a global forum for disease forecasting and surveillance research.
  • RECON learn: a free, open platform for training material on epidemics analysis.
  • epirecipes: an initiative to implement as many different epidemiological models as possible in a variety of computer languages.
  • Stan for epidemiology: a centralized location for all types of work in epidemiology that use the software Stan for Bayesian inference.

Glossary

Review Papers

Terms

  • Index case
    • Definition: The first case in a family or other defined group to come to the attention of the investigator (Porta, 2014).
    • Chinese: 指示病例
  • Primary case
    • Definition: The individual who introduces the disease into the family or group under study. Not necessarily the first diagnosed case in a family or group (Porta, 2014).
    • Chinese: 原发病例
  • Secondary case
    • Definition: A transmission of an infection from an infected person (primary case) to another person who then becomes infected (European Centre for Disease Prevention and Control (ECDC), 2012).
    • Chinese: 二代病例，继发病例，续发病例
  • Latent period
    • Definition: The latent period refers to the period of time between exposure to a disease with successful transmission and the onset of infectiousness (Milwid et al., 2016).
    • Chinese: 潜隐期
  • Incubation period
    • Definition: The incubation period is defined as the period of time between exposure to the disease (if transmission occurs) and the onset of clinical symptoms (Milwid et al., 2016).
    • Chinese: 潜伏期
  • Infectious period
    • Definition: The infectious period is defined as the time interval in which the infected individual is capable of transmitting the disease (Milwid et al., 2016).
    • Chinese: 感染期

The relationship of periods: latent, incubation, and infectious in the SEIR model is illustrated in Figure 1 of Milwid et al. (2016).

  • Generation time (interval)
    • Definition: In modeling, the generation interval refers to the period of time between the onset of the infectious period in a primary case to the onset of the infectious period in a secondary case infected by the primary case (Wallinga & Teunis, 2004; Milwid et al., 2016).
    • Chinese: 世代时间
  • Serial interval
    • Definition: In epidemiology, the serial interval is defined as the period of time between the onset of symptoms in a primary case to the onset of symptoms in a secondary case infected by the primary case (Fine, 2003; Milwid et al., 2016).
    • Chinese: 代际间隔
  • Morbidity (rate)
    • Definition: Morbidity is another term for illness.
    • Chinese: 发病率
  • Mortality (rate)
    • Definition: Mortality is another term for death.
    • Chinese: 死亡率
  • Incidence
    • Definition: Disease incidence is defined by both epidemiologists and modelers as the number of new cases in a population generated within a certain time period (Milwid et al., 2016).
    • Chinese: 发病率
  • Prevalence
    • Definition: Disease prevalence is defined as the number of cases of a disease at a single time point in a population (Milwid et al., 2016).
    • Chinese: 患病率
  • Attack rate
    • Definition: The attack rate describes the proportion of the population that becomes infected over a specified period of time (Milwid et al., 2016).
    • Chinese: 罹患率
  • Clinical attack rate
    • Definition: The clinical attack rate measures the proportion fo the population that develops disease symptoms as a result of an infection (Milwid et al., 2016).
    • Chinese: 临床罹患率
  • Secondary attack rate
    • Definition: The secondary attack rate (SAR) is the probability that infection occurs among susceptible persons within a reasonable incubation period following known contact with an infectious person or another infectious source (Altman et al., 2005).
    • Chinese: 续发率
  • Basic reproduction/reproductive number/ratio
    • Symbol: R_0
    • Definition: the expected number of secondary cases produced by a typical primary case in an entirely susceptible population (Wallinga & Teunis, 2004).
    • Chinese: 基本再生数
  • Effective reproduction/reproductive number/ratio
    • Symbol: R_t
    • Definition: A population will rarely be totally susceptible to an infection in the real world. The effective reproductive number estimates the average number of secondary cases per infectious case at time t in a population made up of both susceptible and non-susceptible hosts.
    • Chinese: 有效再生数
    • Remark: Wallinga & Teunis (2004) proposed a method that is generic and requires only case incidence data and the distribution of the serial interval to estimate effective reproduction number over the course of an epidemic. However, the approach has several drawbacks. First, estimates are right censored, because the estimate of R at time t requires incidence data from times later than t. Approaches to correct for this issue have been developed by Cauchemez et al. (2006). Furthermore, when the data aggregation time step is small (e.g., daily data), estimates of R can vary considerably over short time periods., producting substantial negative autocorrelation. For more details we refer the reader to Cori et al. (2013).
  • Case reproduction number
    • Definition: The case reproduction number is a property of individuals infected at time t, and is the average number of people someone infected at time t can expect to infect. It is sometimes called the cohort reproduction number because it counts the average number of secondary transmissions caused by a cohort infected at time step t (Fraser, 2007; Cori et al., 2013).
    • Chinese: 病例再生数
    • Remark: The case reproduction number is denoted R_c(t) in Fraser (2007) while R^c(t) in Cori et al. (2013). Essentially, It is the widely used effective reproduction number. The case reproduction number is the quantity estimated in the Wallinga and Teunis-type approaches.
  • Instantaneous reproduction number
    • Definition: The instantaneous reproduction number is a property of epidemic at time t, and is the average number of people someone infected at time t could expect to infect should the condition remain unchanged (Fraser, 2007; Cori et al., 2013).
    • Chinese: 瞬时再生数
    • Remark: In both Fraser (2007) and Cori et al. (2013), the instantaneous reproduction number is denoted R(t), which is usually used as the notation for effective reproduction number. The instantaneous reproduction number is the only repproduction number easily estimated in real time. Moreover, effective control measures undertaken at time t are expected to result in a sudden decrease in the instantaneous reproduction number and a smoother decrease in the case reproduction number. Hence, assessing the efficiency of control measures is easier by using estimates of the instantaneous reproduction number.
  • Household reproduction number
    • Definition: The household reproduction number is defined as the number of households infected by each infected household (Fraser, 2007).
    • Chinese: 家庭再生数
  • Vaccine efficacy
    • Definition: In epidemiological and clinical studies, vaccine efficacy refers to the percentage reduction in the attack rate of the vaccinated cohort compared to the unvaccinated cohort as observed in randomized controlled (field) trial (Milwid et al., 2016).
    • Chinese: 疫苗效能
  • Vaccine effectiveness
    • Definition: Vaccine effectiveness refers to the ability of a vaccine to prevent infection or related outcomes in the population in real-world conditions (Milwid et al., 2016).
    • Chinese: 疫苗效果
  • Herd immunity
    • Definition: a form of indirect protection from infectious disease that occurs when a large percentage of a population has become immune to an infection, thereby providing a measure of protection for individuals who are not immune.
    • Chinese: 人群免疫力
  • Herd immunity threshold, Eradication fraction
    • Symbol: S_h
    • Definition: Under a compartmental framwork with homogenous mixing, the minimum fraction of susceptibles that must be immune (or vaccinated at birth (assuming 100% vaccine efficacy)) to reduce R_t below 1 and eradicate infection; that is, by the removal of susceptible hosts (Mishra et al., 2010).
    • Chinese: 群体免疫阈值
  • Epidemic
    • Definition: The occurrence of more cases of disease, injury or other health condition than expected in a given area or among a specific group of persons during a particular period. Usually, the cases are presumed to have a common cause or to be related to one another in some way(Orbann et al., 2017).
    • Chinese: 流行
  • Epidemic final size
    • Definition: TODO(Ma & Earn, 2006; Miller, 2012)
    • Chinese: 流行最终规模
  • Epidemic threshold
    • Definition: TODO
    • Chinese: 流行阈值
  • Epidemic curve
    • Definition: the frequency of new cases over time based on the date of onset of disease.
    • Chinese: 流行曲线
  • Emerging Infectious Disease (EID)
    • Definition: an infectious disease whose incidence has increased in the past 20 years and could increase in the near future.
    • Chinese: 新发传染病
  • Seasonal threshold
    • Definition: TODO
    • Chinese: 季节性阈值
  • Alert threshold
    • Definition: TODO
    • Chinese: 预警阈值
  • Critical community size (CCS)
    • Definition: the minimum size of a closed population within which a human-to-human, non-zoonotic pathogen can persist indefinitely.
    • Chinese: 社区规模临界值

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References

Altman, D. G., Armitage, P., & Colton, T. (2005). Encyclopedia of biostatistics. Encyclopedia of Biostatistics.

Butler, D. (2013). When google got flu wrong. Nature, 494(7436), 155–156. Retrieved from http://www.nature.com/news/when-google-got-flu-wrong-1.12413

Cauchemez, S., Boëlle, P.-Y., Donnelly, C. A., Ferguson, N. M., Thomas, G., Leung, G. M., … Valleron, A.-J. (2006). Real-time estimates in early detection of SARS. Emerging Infectious Diseases, 12(1), 110. Retrieved from http://wwwnc.cdc.gov/eid/article/12/1/05-0593

Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S. (2013). A new framework and software to estimate time-varying reproduction numbers during epidemics. American Journal of Epidemiology, 178(9), 1505–1512. Retrieved from http://aje.oxfordjournals.org/content/178/9/1505.abstract

European Centre for Disease Prevention and Control (ECDC). (2012). Field epidemiology manual. Retrieved from https://wiki.ecdc.europa.eu/fem/Pages/Incubation\%20period,\%20Latent\%20period\%20and\%20Generation\%20time..aspx

Fine, P. E. M. (2003). The interval between successive cases of an infectious disease. American Journal of Epidemiology, 158(11), 1039–1047. Retrieved from http://aje.oxfordjournals.org/content/158/11/1039.abstract

Finkenstädt, B. F., & Grenfell, B. T. (2000). Time series modelling of childhood diseases: A dynamical systems approach. Journal of the Royal Statistical Society: Series C (Applied Statistics), 49(2), 187–205. Retrieved from http://dx.doi.org/10.1111/1467-9876.00187

Fraser, C. (2007). Estimating individual and household reproduction numbers in an emerging epidemic. PLoS ONE, 2(8), e758–. https://doi.org/10.1371/journal.pone.0000758

Freifeld, C. C., Mandl, K. D., Reis, B. Y., & Brownstein, J. S. (2008). HealthMap: Global infectious disease monitoring through automated classification and visualization of internet media reports. Journal of the American Medical Informatics Association, 15(2), 150–157. Retrieved from http://jamia.oxfordjournals.org/content/15/2/150.abstract

Ginsberg, J., Mohebbi, M. H., Patel, R. S., Brammer, L., Smolinski, M. S., & Brilliant, L. (2009). Detecting influenza epidemics using search engine query data. Nature, 457(7232), 1012–1014. Retrieved from http://dx.doi.org/10.1038/nature07634

Groendyke, C., & Welch, D. (2018). Epinet: An r package to analyze epidemics spread across contact networks. J. Stat. Softw., 83(11), 1–22.

Keeling, M. J., & Rohani, P. (2008). Modeling infectious diseases in humans and animals. Princeton University Press.

Lazer, D., Kennedy, R., King, G., & Vespignani, A. (2014). The parable of google flu: Traps in big data analysis. Science, 343(6176), 1203–1205. Retrieved from http://www.sciencemag.org/content/343/6176/1203.short

Ma, J., & Earn, DavidJ. D. (2006). Generality of the final size formula for an epidemic of a newly invading infectious disease. Bull. Math. Biol., 68(3), 679-702-. Retrieved from http://dx.doi.org/10.1007/s11538-005-9047-7

Miller, J. C. (2012). A note on the derivation of epidemic final sizes. Bull. Math. Biol., 74(9), 2125–2141. Retrieved from https://doi.org/10.1007/s11538-012-9749-6

Milwid, R., Steriu, A., Arino, J., Heffernan, J., Hyder, A., Schanzer, D., … Moghadas, S. M. (2016). Toward standardizing a lexicon of infectious disease modeling terms. Frontiers in Public Health, 4, 213. Retrieved from http://journal.frontiersin.org/article/10.3389/fpubh.2016.00213

Mishra, S., Fisman, D. N., & Boily, M.-C. (2010). The ABC of terms used in mathematical models of infectious diseases. Journal of Epidemiology and Community Health. Retrieved from http://jech.bmj.com/content/early/2010/10/21/jech.2009.097113.abstract

Mossong, J., Hens, N., Jit, M., Beutels, P., Auranen, K., Mikolajczyk, R., … Edmunds, W. J. (2008). Social contacts and mixing patterns relevant to the spread of infectious diseases. PLoS Med, 5(3), e74–. https://doi.org/10.1371/journal.pmed.0050074

Obadia, T., Haneef, R., & Boelle, P.-Y. (2012). The R0 package: A toolbox to estimate reproduction numbers for epidemic outbreaks. BMC Med. Inform. Decis. Mak., 12(1), 147–. Retrieved from http://www.biomedcentral.com/1472-6947/12/147

Orbann, C., Sattenspiel, L., Miller, E., & Dimka, J. (2017). Defining epidemics in computer simulation models: How do definitions influence conclusions? Epidemics, 19, 24–32. Retrieved from http://www.sciencedirect.com/science/article/pii/S1755436516300627

Panhuis, W. G. van, Grefenstette, J., Jung, S. Y., Chok, N. S., Cross, A., Eng, H., … Burke, D. S. (2013). Contagious diseases in the united states from 1888 to the present. New England Journal of Medicine, 369(22), 2152–2158. https://doi.org/10.1056/NEJMms1215400

Porta, M. (2014). A dictionary of epidemiology. Oxford university press.

Squires, R. B., Noronha, J., Hunt, V., García-Sastre, A., Macken, C., Baumgarth, N., … Scheuermann, R. H. (2012). Influenza research database: An integrated bioinformatics resource for influenza research and surveillance. Influenza and Other Respiratory Viruses, 6(6), 404–416. Retrieved from http://dx.doi.org/10.1111/j.1750-2659.2011.00331.x

Vega, T., Lozano, J. E., Meerhoff, T., Snacken, R., Beauté, J., Jorgensen, P., … Nielsen, J. (2015). Influenza surveillance in europe: Comparing intensity levels calculated using the moving epidemic method. Influenza and Other Respiratory Viruses, 9(5), 234–246.

Vega, T., Lozano, J. E., Meerhoff, T., Snacken, R., Mott, J., Ortiz de Lejarazu, R., & Nunes, B. (2013). Influenza surveillance in europe: Establishing epidemic thresholds by the moving epidemic method. Influenza and Other Respiratory Viruses, 7(4), 546–558.

Wallinga, J., & Teunis, P. (2004). Different epidemic curves for severe acute respiratory syndrome reveal similar impacts of control measures. American Journal of Epidemiology, 160(6), 509–516. Retrieved from http://aje.oxfordjournals.org/content/160/6/509.abstract

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