Computational Epidemiology

table of contents introduction  overview   I. infectious diseases   a brief history of infectious diseases classical infectious diseases smallpox, polio, measles, rubella, influenza corona virus type diseases SARS, MERS, COVID-19 statistic vs. mechanistic modeling data science vs. data-driven modeling examples: the measles reading: bar-on et al., SARS-CoV-2 (COVID-19) by the numbers, elife 9 (2020) e57309.   II. mathematical epidemiology   II.1. introduction to compartment modeling   concept of compartment modeling the kermack-mc kendrick theory the classical S,I,R model SIR model with and without vital dynamics examples: the plaque reading: bauer f, compartment models in epidemiology, mathematical epidemiology (2008) 19-79.   II.2. compartment modeling of epidemiology  overview of compartment models the M, S, E, I, R, D compartments SIR, SIS, SIRD, MSIR, SEIR, MSEIR, MSEIRS models latent, contact, and infectious periods examples: the measles reading: hethcode hw, the mathematics of infectious disease, siam review 42 (2020) 599-653.   II.3. concepts of endemic disease modeling     concept of basic reproduction number endemic equilibrium herd immunity eradicating disease through vaccination examples: measles reading: dietz k, the estimation of the basic reproduction number for infectious diseases, stat meth med res 2 (1993) 23-41.   III. data-driven modeling in epidemiology   III.1. compartment modeling of COVID19 characteristic timeline of COVID-19 SIR and SEIR models for COVID-19 susceptible, exposed, infectious, and recovered populations latent, contact, and infectious periods of COVID-19 examples: sensitivity analysis for COVID-19 reading: peirlinck m, et al. outbreak dynamics of COVID-19 in china and the united states. biomech model mechanobio 19 (2020) 2179-2193.   III.2. early outbreak dynamics of COVID-19 basic reproduction number of COVID-19 SEIR model and parameter identification of Ro comparison with other infectious diseases and with directly measured Ro implications for exponential growth and herd immunity examples: parameter identification for china and the united states reading: park et al., reconciling early-outbreak estimates of the basic reproduction number and its uncertainty. j royal soc interface 17 (2020) 20200144.   III.3. asymptomatic transmission of COVID-19 concept of asymptomatic transmission SEIIR model antibody seroprevalence studies undercount and its implications on herd immunity examples: santa clara county, new york city, heinsberg reading: ioannis j, the invection fatality rate of COVID-19 inferred from seroprevalence data, medRxiv, doi:10.1101/2020.05.13.20101253   III.4. inferring outbreak dynamics of COVID-19 concept of data-driven modeling bayesian SEIIR model machine learning and bayesian methods uncertainty quantification inferring the beginning of the outbreak examples: santa clara county reading: peirlinck m et al., visualizing the invisible: the effect of asymptomatic transmission. comp meth appl mech eng. 372 (2020) 113410.   IV. modeling outbreak control   IV.1. managing infectious diseases overview of community mitigation strategies ethical implications of political countermeasures concept of nowcasting basic and effective reproduction numbers Ro and Rt                               examples: china, europe, united states reading: wilder-smith a, freedman do. isolation, quarantine, social distancing and community containment, j travel med (2020) 1-4.   IV.2. change-point modeling of COVID-19 concept of change points interval-type compartment models for COVID-19 discretely vs continuously changing transition rates learning change points examples: COVID-19 dynamics in germany reading: dehning et al., inferring change points in the spread of COVID-19 reveals the effectiveness of interventions, science doi:10.1126/science.abb9789   IV.3. dynamic compartment modeling of COVID-19 concept of flattening the curve bayesian dynamic SEIR model time-dependent contact rate, hyperbolic tangent vs. random walk learning the time-varying effective reproduction number Rt examples: Ro and Rt in europe reading: linka et al., the reproduction number of COVID-19 and its correlation with public health interventions, comp mech. 66 (2020) 1035-1050.   V. network modeling of epidemiology   V.1. network modeling of epidemic processes  concept of network modeling directed graphs, shortest path, small world networks adjacency, degree, graph Laplacian network modeling of epidemiology examples: network models of europe and the united states reading: pastor-satorras r et al., epidemic processes in complex networks, rev mod phys 87 (2015) 926-973.   V.2. network modeling of COVID-19 concept of reaction-diffusion modeling network SEIR model for COVID-19 network vs. continuum modeling of COVID-19 spread air traffic mobility networks and spreading patterns examples: early COVID-19 spreading across the european union reading: linka k et al. outbreak dynamics of COVID-19 in europe and the effect of travel restrictions. comp meth biomech biomed eng; 2020; 23:710-717.   V.3. dynamic network modeling of COVID-19 concept of disease management via constrained mobility dynamic network SEIR model for COVID-19 mobility networks of walking, car, transit, air traffic correlating mobility and reproduction examples: mobility and reproduction number in the european union reading: linka k et al. global and local mobility as a barometer for COVID-19 dynamics. biomech model mechanobio (2020) doi:10.1007/s10237-020-01408-2.   VI. informing political decision making through modeling   VI.1 exit strategies from lockdown concept of travel restrictions dynamic network mobility SEIR model travel bubbles to safely lift travel bans restricted travel vs. quarantine example: newfoundland, canada, north america reading: linka k et al. is it safe to lift COVID-19 travel bans. the newfoundland story. comp mech. 66 (2020) 1081–1092.   VI.2. vaccination strategies concept of vaccination towards herd immunity or eradication SEIR model for COVID-19 vaccination strategies of test-trace-isolate estimating herd immunity and tracing thresholds for COVID-19 example: learning from eradicating smallpox reading: anderson rm, may rm, directly transmitted infectious diseases: control by vaccination, science 215 (1982) 1053-1060.   VI.3. the second wave concept of seasonality seasonal SEIR model basic reproduction number of seasonal infectious disease seasonality of mobility, seasonal workers, tourism, behavioral changes example: seasonality of COVID-19                    reading: grassly nc, fraser c, seasonal infectious disease epidemiology, proc royal soc b 273 (2006) 2541-2550.                            lessons learned COVID-19 is spreads exponentially if uncontrolled COVID-19 is as contagious as previous coronaviruses without vaccination, COVID-19 will be with us for a long time we can flatten the curve and model it constraining mobility is drastic but effective reproduction is correlated to mobility with a delay of two weeks most COVID-19 cases are asymptomatic and unreported  COVID-19 generates a ton of data, but not always suited for models selective reopening can be more effective than quarantine testing is critical for safe reopening reading: kuhl e. data-driven modeling of COVID-19 - lessons learned. extr mech lett. 40 (2020) 100921.   potentially additional topics             superspreading concept of heterogeneity dispersion parameter k superspreading events of COVID-19 implications for outbreak control example: superspreading events in churches and meat factories   reading: lloyd-smith jo et al., superspreading and the effect of individual variation on disease emergence, nature 438 (2005) 355-359.   heterogeneous mixing concept of population heterogeneity SEIR model of mixing age-specific modeling implications for outbreak control example: role of children in COVID-19 transmission reading: britton t et al., mathematical model reveals the influence of population heterogeneity on herd immunity to SARS-CoV-2, science 369 (2020) 846-849.