Forecasting epidemic dynamics has been an active area of research for at least two decades. The importance of the topic is evident: policy makers, citizens, and scientists would all like to get accurate and timely forecasts. In contrast to physical systems, the co-evolution of epidemics, individual and collective behavior, viral dynamics, and public policies make epidemic forecasting a problematic task. The situation is even more challenging during a pandemic as has become amply clear during the ongoing COVID-19 pandemic. Researchers worldwide have put in extraordinary efforts to try to forecast the time-varying evolution of the pandemic; despite their best efforts, it is fair to say that the results have been mixed. Several teams have done well on average but failed to forecast upsurges in the cases.
In this chapter, we describe the state-of-the-art in epidemic forecasting, with a particular emphasis on forecasting during an ongoing pandemic. We describe a range of methods that have been developed and discuss the experience of our team in this context. We also summarize several challenges in producing accurate and timely forecasts.
This is a preview of subscription content, access via your institution.
Tax calculation will be finalised at checkout
Purchases are for personal use onlyLearn about institutional subscriptions
The unit of time (which may be a day, a week, etc.) depends on the epidemic that is being modeled.
This is analogous to the transmission rate β introduced under the compartmental model above.
With minor modifications, our theoretical results discussed in section “Theoretical Foundations for Forecasting in Network Models” can be shown to hold even when the infectious period for each node is any constant number of time units.
A Boolean formula in conjunctive normal form  consists of clauses which are connected together by the AND operator. Each clause itself is the OR of negated or unnegated forms of a subset of variables.
A matching M of a bipartite graph H(V1, V2, E) is a subset of edges so that no two edges are incident on the same node. When ∣V1 ∣ = ∣ V2 ∣ = n, a perfect matching of H is a matching of size n.
A Boolean formula in disjunctive normal form consists of product terms which are connected together by the OR operator. Each product term is the AND of a subset of negated and/or unnegated variables.
Barrios JM, Hochberg YV. Risk perceptions and politics: evidence from the COVID-19 pandemic. J Financ Econ. 2021;142(2):862–79.
Brzezinski A, Kecht V, Van Dijcke D, Wright AL. Science skepticism reduced compliance with COVID-19 shelter-in-place policies in the United States. Nat Hum Behav. 2021;5(11):1519–27.
Fancourt D, Steptoe A, Wright L. The Cummings effect: politics, trust, and behaviours during the COVID-19 pandemic. Lancet. 2020;396(10249):464–5.
Harman JL, Weinhardt JM, Beck JW, Mai I. Interpreting time-series COVID data: reasoning biases, risk perception, and support for public health measures. Sci Rep. 2021;11(1):1–11.
Levin R, Chao DL, Wenger EA, Proctor JL. Insights into population behavior during the COVID-19 pandemic from cell phone mobility data and manifold learning. Nat Computat Sci. 2021;1(9):588–97.
Van Bavel JJ, Cichocka A, Capraro V, Sjåstad H, Nezlek JB, Pavlović T, Alfano M, Gelfand MJ, Azevedo F, Birtel MD, et al. National identity predicts public health support during a global pandemic. Nat Commun. 2022;13(1):1–14.
Woods ET, Schertzer R, Greenfeld L, Hughes C, Miller-Idriss C. COVID-19, nationalism, and the politics of crisis: a scholarly exchange. Nations National. 2020;26(4):807–25.
Adiga A, Dubhashi D, Lewis B, Marathe M, Venkatramanan S, Vullikanti A. Mathematical models for COVID-19 pandemic: a comparative analysis. J Indian Inst Sci. 2020:1–15.
Borchering RK, Viboud C, Howerton E, Smith CP, Truelove S, Runge MC, Reich NG, Contamin L, Levander J, Salerno J, et al. Modeling of future COVID-19 cases, hospitalizations, and deaths, by vaccination rates and nonpharmaceutical intervention scenarios—United States, April–September 2021. Morb Mortal Wkly Rep. 2021;70(19):719.
Chen J, Levin S, Eubank S, Mortveit H, Venkatramanan S, Vullikanti A, Marathe M. Networked epidemiology for COVID-19. SIAM News, June 2020.
Eletreby R, Zhuang Y, Carley KM, Yagan O, Poor HV. The effects of evolutionary adaptations on spreading processes in complex networks. Proc Natl Acad Sci. 2020;117(11):5664–70.
Hu B, Guo H, Zhou P, Shi Z-L. Characteristics of SARS-CoV-2 and COVID-19. Nat Rev Microbiol. 2021;19(3):141–54.
Saad-Roy CM, Morris SE, Metcalf CJE, Mina MJ, Baker RE, Farrar J, Holmes EC, Pybus OG, Graham AL, Levin SA, Grenfell BT, Wagner CE. Epidemiological and evolutionary considerations of SARS-CoV-2 vaccine dosing regimes. Science. 2021;372(6540):363–70.
Wagner CE, Saad-Roy CM, Morris SE, Baker RE, Mina MJ, Farrar J, Holmes EC, Pybus OG, Graham AL, Emanuel EJ, et al. Vaccine nationalism and the dynamics and control of SARS-CoV-2. Science. 2021;373(6562):eabj7364.
Yagan O, Sridhar A, Eletreby R, Levin S, Plotkin JB, Poor HV. Modeling and analysis of the spread of COVID-19 under a multiple-strain model with mutations. Harvard Data Science Review. 2021;4. https://doi.org/10.1162/99608f92.a11bf693. URL https://hdsr.mitpress.mit.edu/pub/2q9jiymv.
Biggerstaff M, Alper D, Dredze M, Fox S, Fung IC-H, Hickmann KS, Lewis B, Rosenfeld R, Shaman J, Tsou M-H, et al. Results from the centers for disease control and prevention’s predict the 2013–2014 influenza season challenge. BMC Infect Dis. 2016;16(1):1–10.
Biggerstaff M, Johansson M, Alper D, Brooks LC, Chakraborty P, Farrow DC, Hyun S, Kandula S, McGowan C, Ramakrishnan N, et al. Results from the second year of a collaborative effort to forecast Influenza seasons in the United States. Epidemics. 2018;24:26–33.
McGowan CJ, Biggerstaff M, Johansson M, Apfeldorf KM, Ben-Nun M, Brooks L, Convertino M, Erraguntla M, Farrow DC, Freeze J, et al. Collaborative efforts to forecast seasonal Influenza in the United States, 2015–2016. Sci Rep. 2019;9(1):1–13.
Reich NG, Brooks LC, Fox SJ, Kandula S, McGowan CJ, Moore E, Osthus D, Ray EL, Tushar A, Yamana TK, et al. A collaborative multiyear, multimodel assessment of seasonal Influenza forecasting in the United States. Proc Natl Acad Sci. 2019;116(8):3146–54.
DARPA. CHIKV Challenge announces winners, progress toward forecasting the spread of infectious diseases. 2015. http://www.darpa.mil/news-events/2015-05-27.
Muthiah S, Butler P, Khandpur RP, Saraf P, Self N, Rozovskaya A, Zhao L, Cadena J, Lu C, Vullikanti A, Marathe A, Summers KM, Katz G, Doyle A, Arredondo J, Gupta DK, Mares D, Ramakrishnan N. EMBERS at 4 years: experiences operating an open source indicators forecasting system. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining; 2016. p. 205–14.
Adiga A, Wang L, Hurt B, Peddireddy A, Porebski P, Venkatramanan S, Lewis B, Marathe MV. All models are useful: Bayesian ensembling for robust high resolution COVID-19 forecasting. In: KDD ‘21: proceedings of the 27th ACM SIGKDD conference on knowledge discovery and data mining; 2021. p. 2505–13.
Chakraborty P, Lewis B, Eubank S, Brownstein JS, Marathe M, Ramakrishnan N. What to know before forecasting the Flu. PLoS Comput Biol. 2018;14(10):e1005964.
Tabataba FS, Chakraborty P, Ramakrishnan N, Venkatramanan S, Chen J, Lewis B, Marathe M. A framework for evaluating epidemic forecasts. BMC Infect Dis. 2017;17(1):345.
Tabataba FS, Lewis B, Hosseinipour M, Tabataba FS, Venkatramanan S, Chen J, Higdon D, Marathe M. Epidemic forecasting framework combining agent-based models and smart beam particle filtering. In: 2017 IEEE international conference on data mining (ICDM). IEEE; 2017. p. 1099–104.
Drake JM. Fundamental limits to the precision of early warning systems for epidemics of infectious diseases. PLoS Med. 2005;2:461–2.
Drake JM. Limits to forecasting precision for outbreaks of directly transmitted diseases. PLoS Med. 2006;3:57–62.
May RM. Network structure and the biology of populations. Trends Ecol Evol. 2006;21(7):394–9.
Jacob F. Evolution and tinkering. Science. 1977;196:1161–6.
Rosenkrantz DJ, Vullikanti A, Ravi SS, Stearns RE, Levin S, Poor HV, Marathe MV. Fundamental limitations on efficiently forecasting epidemic measures in network models. Proc Nat Acad Sci (PNAS). 2022;119(4):1–7.
Nsoesie E, Mararthe M, Brownstein J. Forecasting peaks of seasonal Influenza epidemics. PLoS Curr. 2013;5.
Yang S, Santillana M, Kou SC. Accurate estimation of Influenza epidemics using Google search data via ARGO. Proc Natl Acad Sci. 2015;112(47):14473–8.
Rangarajan P, Mody SK, Marathe M. Forecasting Dengue and Influenza incidences using a sparse representation of Google trends, electronic health records, and time series data. PLoS Comput Biol. 2019;15(11):e1007518.
Kandula S, Hsu D, Shaman J. Subregional nowcasts of seasonal Influenza using search trends. J Med Internet Res. 2017;19(11):e370.
Soebiyanto RP, Adimi F, Kiang RK. Modeling and predicting seasonal Influenza transmission in warm regions using climatological parameters. PLoS One. 2010;5(3):e9450.
Paul MJ, Dredze M, Broniatowski D. Twitter improves Influenza forecasting. PLoS Curr. 2014;6.
Wang Z, Chakraborty P, Mekaru SR, Brownstein JS, Ye J, Ramakrishnan N. Dynamic Poisson autoregression for Influenza-like-illness case count prediction. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining; 2015. p. 1285–94.
Dugas AF, Jalalpour M, Gel Y, Levin S, Torcaso F, Igusa T, Rothman RE. Influenza forecasting with Google Flu Trends. PLoS One. 2013;8(2):e56176.
Radin JM, Wineinger NE, Topol EJ, Steinhubl SR. Harnessing wearable device data to improve state-level real-time surveillance of Influenza-like illness in the USA: a population-based study. Lancet Digit Health. 2020;2(2):e85–93.
Tibshirani R. Regression shrinkage and selection via the Lasso. J R Statist Soc Ser B (Methodol). 1996;58(1):267–88.
Tseng P. Convergence of a block coordinate descent method for nondifferentiable minimization. J Optim Theory Appl. 2001;109(3):475–94.
Brooks LC, Farrow DC, Hyun S, Tibshirani RJ, Rosenfeld R. Flexible modeling of epidemics with an empirical bayes framework. PLoS Comput Biol. 2015;11(8):e1004382.
Viboud C, Boëlle P-Y, Carrat F, Valleron A-J, Flahault A. Prediction of the spread of Influenza epidemics by the method of analogues. Am J Epidemiol. 2003;158(10):996–1006.
Van Panhuis WG, Hyun S, Blaney K, Marques ET Jr, Coelho GE, Siqueira JB Jr, Tibshirani R, da Silva Jr JB, Rosenfeld R. Risk of Dengue for tourists and teams during the World Cup 2014 in Brazil. PLoS Negl Trop Dis. 2014;8(7):e3063.
Petropoulos F, Makridakis S. Forecasting the novel coronavirus COVID-19. PLoS One. 2020;15(3):e0231236.
Evensen G. Data assimilation: the ensemble Kalman filter. Springer; 2009.
Anderson JL. An ensemble adjustment Kalman filter for data assimilation. Mon Weather Rev. 2001;129(12):2884–903.
Arulampalam MS, Maskell S, Gordon N, Clapp T. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Trans Signal Process. 2002;50(2):174–88.
Yang W, Karspeck A, Shaman J. Comparison of filtering methods for the modeling and retrospective forecasting of Influenza epidemics. PLoS Comput Biol. 2014;10(4):e1003583.
Shaman J, Karspeck A. Forecasting seasonal outbreaks of Influenza. Proc Natl Acad Sci. 2012;109(50):20425–30.
Shaman J, Karspeck A, Yang W, Tamerius J, Lipsitch M. Real-time Influenza forecasts during the 2012–2013 season. Nat Commun. 2013;4:2837.
Yang W, Cowling BJ, Lau EH, Shaman J. Forecasting Influenza epidemics in Hong Kong. PLoS Comput Biol. 2015;11(7):e1004383.
Hornik K, Stinchcombe M, White H. Multilayer feedforward networks are universal approximators. Neural Netw. 1989;2(5):359–66.
Aburas HM, Cetiner BG, Sari M. Dengue confirmed-cases prediction: a neural network model. Expert Syst Appl. 2010;37(6):4256–60.
Wahyunggoro O, Permanasari AE, Chamsudin A. Utilization of neural network for disease forecasting. In: 59th ISI world statistics congress. Citeseer; 2013. p. 549–54.
Xu Q, Gel YR, Ramirez LL, Nezafati K, Zhang Q, Tsui K-L. Forecasting Influenza in Hong Kong with Google search queries and statistical model fusion. PLoS One. 2017;12(5):e0176690.
Chung J, Gulcehre C, Cho K, Bengio Y. Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555. 2014.
Hochreiter S, Schmidhuber J. Long short-term memory. Neural Comput. 1997;9(8):1735–80.
Volkova S, Ayton E, Porterfield K, Corley CD. Forecasting Influenza-like illness dynamics for military populations using neural networks and social media. PLoS One. 2017;12(12):e0188941.
Venna SR, Tavanaei A, Gottumukkala RN, Raghavan VV, Maida AS, Nichols S. A novel data-driven model for real-time Influenza forecasting. IEEE Access. 2019;7:7691–701.
Zhu X, Fu B, Yang Y, Ma Y, Hao J, Chen S, Liu S, Li T, Liu S, Guo W, et al. Attention-based recurrent neural network for Influenza epidemic prediction. BMC Bioinform. 2019;20(18):1–10.
Adhikari B, Xu X, Ramakrishnan N, Prakash BA. EpiDeep: exploiting embeddings for epidemic forecasting. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining; 2019. p. 577–86.
Rodriguez A, Tabassum A, Cui J, Xie J, Ho J, Agarwal P, Adhikari B, Prakash BA. DeepCOVID: an operational deep learning-driven framework for explainable real-time COVID-19 forecasting. medRxiv. 2020.
Chimmula VKR, Zhang L. Time series forecasting of COVID-19 transmission in Canada using LSTM networks. Chaos, Solitons & Fractals; 2020. p. 109864.
Wu Y, Yang Y, Nishiura H, Saitoh M. Deep learning for epidemiological predictions. In: The 41st international ACM SIGIR conference on research & development in information retrieval. ACM; 2018. p. 1085–8.
Ramchandani A, Fan C, Mostafavi A. DeepCOVIDNet: an interpretable deep learning model for predictive surveillance of COVID-19 using heterogeneous features and their interactions. arXiv preprint arXiv:2008.00115. 2020.
Deng S, Wang S, Rangwala H, Wang L, Ning Y. Cola-GNN: cross-location attention based graph neural networks for long-term ILI prediction. In: Proceedings of the 29th ACM international conference on information and knowledge management; 2020. p. 245–54.
Kapoor A, Ben X, Liu L, Perozzi B, Barnes M, Blais M, O’Banion S. Examining COVID-19 forecasting using spatio-temporal graph neural networks. arXiv preprint arXiv:2007.03113. 2020.
Wang L, Ben X, Adiga A, Sadilek A, Tendulkar A, Venkatramanan S, Vullikanti A, Aggarwal G, Talekar A, Chen J, et al. Using mobility data to understand and forecast COVID-19 dynamics. In: IJCAI 2021 workshop on AI for social good; 2021.
Wang L, Adiga A, Chen J, Lewis B, Sadilek A, Venkatramanan S, Marathe M. Combining theory and data driven approaches for epidemic forecasts. CRC Press (to appear). 2022. https://sites.google.com/vt.edu/sgml-book.
Zhao L, Chen J, Chen F, Wang W, Lu C-T, Ramakrishnan N. Simnest: social media nested epidemic simulation via online semi-supervised deep learning. In: 2015 IEEE international conference on data mining. IEEE; 2015. p. 639–48.
Hua T, Reddy CK, Zhang L, Wang L, Zhao L, Lu C-T, Ramakrishnan N. Social media based simulation models for understanding disease dynamics. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence, IJCAI-18, International Joint Conferences on Artificial Intelligence Organization; 2018. p. 3797–804.
Wang L, Chen J, Marathe M. DEFSI: deep learning based epidemic forecasting with synthetic information. In: Proceedings of the AAAI conference on artificial intelligence, vol. 33; 2019. p. 9607–12.
Dandekar R, Rackauckas C, Barbastathis G. A machine learning-aided global diagnostic and comparative tool to assess effect of quarantine control in COVID-19 spread. Patterns. 2020;1(9):100145.
Gao J, Sharma R, Qian C, Glass LM, Spaeder J, Romberg J, Sun J, Xiao C. STAN: spatio-temporal attention network for pandemic prediction using real-world evidence. J Am Med Inform Assoc. 2021;28(4):733–43.
Ray EL et al. Challenges in training ensembles to forecast COVID-19 cases and deaths in the United States. International Institute of Forecasters; 2021.
Morgan JJ, Wilson OC, Menon PG. The wisdom of crowds approach to Influenza-rate forecasting. In: ASME international mechanical engineering congress and exposition, vol. 52026, page V003T04A048. American Society of Mechanical Engineers; 2018.
Taylor KS, Taylor JW. Harnessing the wisdom of the crowd to forecast incident and cumulative COVID-19 mortality in the United States. medRxiv. 2021.
Farrow DC, Brooks LC, Hyun S, Tibshirani RJ, Burke DS, Rosenfeld R. A human judgment approach to epidemiological forecasting. PLoS Comput Biol. 2017;13(3):e1005248.
Li EY, Tung C-Y, Chang S-H. The wisdom of crowds in action: forecasting epidemic diseases with a web-based prediction market system. Int J Med Inform. 2016;92:35–43.
Cheng J, Adamic LA, Kleinberg JM, Leskovec J. Do cascades recur? In: Proceedings of the 25th international conference on world wide web, WWW 2016, Montreal, Canada, April 11–15; 2016. p. 671–81.
Martin T, Hofman JM, Sharma A, Anderson A, Watts DJ. Exploring limits to prediction in complex social systems. In: Proceedings of the 25th international conference on world wide web, WWW 2016, Montreal, Canada, April 11–15, 2016. p. 683–94.
Hofman JM, Sharma A, Watts DJ. Prediction and explanation in social systems. Science. 2017;355:486–8.
Lazer D, Kennedy R, King G, Vespignani A. The parable of Google Flu: traps in Big Data analysis. Science. 2014;343:1203–5.
Pinto PC, Thiran P, Vetterli M. Locating the source of diffusion in large-scale networks. Phys Rev Lett. 2012;109(6):1–4.
Karrer B, Newman ME. Message passing approach for general epidemic models. Phys Rev E. 2010;82(1):016101.
Altarelli F, Braunstein A, Dall’Asta L, Lage-Castellanos A, Zecchina R. Bayesian inference of epidemics on networks via Belief Propagation. Phys Rev Lett. 2014;112:118701.
Lokhov AY, Mézard M, Ohta H, Zdeborová L. Inferring the origin of an epidemic with a dynamic message-passing algorithm. Phys Rev E. 2014;90:012801.
Althouse BM, Wenger EA, Miller JC, Scarpino SV, Allard A, Hébert-Dufresne L, Hu H. Superspreading events in the transmission dynamics of SARS-CoV-2: opportunities for interventions and control. PLoS Biol. 2020;18(11):e3000897.
Scarpino SV, Petri G. On the predictability of infectious disease outbreaks. Nat Commun. 2019;10:1–8. https://doi.org/10.1038/s41467-019-08616-0.
Brauer F, van den Driessche P, Wu J. Mathematical epidemiology, volume 1945 of Springer Verlag, Lecture Notes in Mathematics. Springer; 2008.
Marathe MV, Vullikanti AK. Computational Epidemiology. Commun ACM. 2013;56(7):88–96.
Newman M. The structure and function of complex networks. SIAM Rev. 2003;45(2):167–256.
Lipsitch M, Cohen T, Cooper B, Robins JM, Ma S, James L, Gopalakrishna G, Chew S, Tan CC, Samore MH, Fisman D, Murray M. Transmission dynamics and control of severe acute respiratory syndrome. Science. 2003;300:1966–70.
Gneiting T, Raftery AE. Strictly proper scoring rules, prediction, and estimation. J Am Stat Assoc. 2007;102(477):359–78.
Bracher J, Ray EL, Gneiting T, Reich NG. Evaluating epidemic forecasts in an interval format. PLoS Comput Biol. 2021;17(2):e1008618.
Arnold T, Bien J, Brooks L, Colquhoun S, Farrow D, Grabman J, Maynard-Zhang P, Reinhart A, Tibshirani R. Covidcast: client for Delphi’s COVIDcast epidata API. 2021. URL https://cmu-delphi.github.io/covidcast/covidcastR/. R package version 0.4.2.
Viboud C, Sun K, Gaffey R, Ajelli M, Fumanelli L, Merler S, Zhang Q, Chowell G, Simonsen L, Vespignani A, et al. The RAPIDD Ebola forecasting challenge: synthesis and lessons learnt. Epidemics. 2018;22:13–21.
Yamana TK, Kandula S, Shaman J. Superensemble forecasts of Dengue outbreaks. J R Soc Interface. 2016;13(123):20160410.
Burgers G, Jan van Leeuwen P, Evensen G. Analysis scheme in the ensemble Kalman filter. Mon Weather Rev. 1998;126(6):1719–24.
Gal Y, Ghahramani Z. Dropout as a Bayesian approximation: representing model uncertainty in deep learning. In: International conference on machine learning; 2016. p. 1050–9.
Kiefer J. Sequential minimax search for a maximum. Proc Am Math Soc. 1953;4(3):502–6.
Raftery AE, Gneiting T, Balabdaoui F, Polakowski M. Using Bayesian model averaging to calibrate forecast ensembles. Mon Weather Rev. 2005;133(5):1155–74.
Yamana TK, Kandula S, Shaman J. Individual versus superensemble forecasts of seasonal Influenza outbreaks in the United States. PLoS Comput Biol. 2017;13(11):e1005801.
Bilmes JA, et al. A gentle tutorial of the EM algorithm and its application to parameter estimation for Gaussian mixture and hidden Markov models. Int Comp Sci Inst. 1998;4(510):126.
Dempster AP, Laird NM, Rubin DB. Maximum likelihood from incomplete data via the EM algorithm. J R Statist Soc Se B (Methodol). 1977;39(1):1–22.
Hoeting JA, Madigan D, Raftery AE, Volinsky CT. Bayesian model averaging: a tutorial. Statist Sci. 1999;14(4):382–401.
COVID-Hub. The COVID-19 Forecast Hub community. 2021. https://covid19forecasthub.org/.
Bai L, Yao L, Li C, Wang X, Wang C. Adaptive graph convolutional recurrent network for traffic forecasting. arXiv preprint arXiv:2007.02842. 2020.
Lai G, Chang W-C, Yang Y, Liu H. Modeling long-and short-term temporal patterns with deep neural networks. In: The 41st international ACM SIGIR conference on research & development in information retrieval; 2018. p. 95–104.
Li Y, Yu R, Shahabi C, Liu Y. Diffusion convolutional recurrent neural network: data-driven traffic forecasting. arXiv preprint arXiv:1707.01926. 2017.
Wu Z, Pan S, Long G, Jiang J, Zhang C. Graph wavenet for deep spatial-temporal graph modeling. arXiv preprint arXiv:1906.00121. 2019.
Wu Z, Pan S, Long G, Jiang J, Chang X, Zhang C. Connecting the dots: multivariate time series forecasting with graph neural networks. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining; 2020. p. 753–63.
Yu B, Yin H, Zhu Z. Spatio-temporal graph convolutional networks: a deep learning framework for traffic forecasting. arXiv preprint arXiv:1709.04875. 2017.
Karpatne A, Atluri G, Faghmous JH, Steinbach M, Banerjee A, Ganguly A, Shekhar S, Samatova N, Kumar V. Theory-guided data science: a new paradigm for scientific discovery from data. IEEE Trans Knowl Data Eng. 2017;29(10):2318–31.
Wang L, Adiga A, Chen J, Sadilek A, Venkatramanan S, Marathe M. CausalGNN: causal-based graph neural networks for spatio-temporal epidemic forecasting. In: Proceedings of the 36th AAAI conference in artificial intelligence (to appear). AAAI Press; 2022.
Garey MR, Johnson DS. Computers and intractability: a guide to the theory of NP-completeness. San Francisco, CA: W. H. Freeman and Co.; 1979.
Valiant LG. The complexity of enumeration and reliability problems. SIAM J Comput. 1979;8(3):410–21.
Arora S, Barak B. Computational complexity: a modern approach. New York, NY: Cambridge University Press; 2009.
Vadhan S. The complexity of counting in sparse, regular and planar graphs. SIAM J Comput. 2001;31(2):398–427.
Karp RM, Luby M. Monte-Carlo algorithms for the planar multiterminal network reliability problem. J Complex. 1985;1(1):45–64.
Saha S, Adiga A, Prakash BA, Vullikanti AKS. Approximation algorithms for reducing the spectral radius to control epidemic spread. In: Venkatasubramanian S, Ye J, editors. Proceedings of the 2015 SIAM international conference on data mining, Vancouver, BC, Canada, April 30—May 2, 2015. SIAM; 2015. p. 568–76.
Sambaturu P, Adhikari B, Prakash BA, Venkatramanan S, Vullikanti A. Designing effective and practical interventions to contain epidemics. In: Seghrouchni AEF, Sukthankar G, An B, Yorke-Smith N, editors. Proceedings of the 19th international conference on autonomous agents and multiagent systems, AAMAS ‘20, Auckland, New Zealand, May 9–13, 2020. International Foundation for Autonomous Agents and Multiagent Systems; 2020. p. 1187–95.
Shah D, Zaman T. Detecting sources of computer viruses in networks: theory and experiment. In: SIGMETRICS 2010, proceedings of the 2010 ACM SIGMETRICS international conference on measurement and modeling of computer systems, New York, New York, USA, 14–18 June 2010. ACM; 2010. p. 203–14.
Wagner MM, Moore AW, Aryel RM. Handbook of biosurveillance. Elsevier; 2011.
Dong E, Du H, Gardner L. An interactive web-based dashboard to track COVID-19 in real time. Lancet Infect Dis. 2020;20(5):533–4.
Robishaw JD, Alter SM, Solano JJ, Shih RD, DeMets DL, Maki DG, Hennekens CH. Genomic surveillance to combat COVID-19: challenges and opportunities. Lancet Microbe. 2021;2(9):e481–4.
The COVID-19 Genomics UK (COG-UK) Consortium. An integrated national scale SARS-CoV-2 genomic surveillance network. The Lancet Microbe. 2020;1(3):e99.
Shu Y, McCauley J. GISAID: global initiative on sharing all Influenza data–from vision to reality. Eur Secur. 2017;22(13):30494.
We thank members of the Biocomplexity COVID-19 Response Team and the Network Systems Science and Advanced Computing (NSSAC) Division of the University of Virginia for their thoughtful comments and suggestions related to epidemic modeling and response support. This work has been partially supported by DTRA (Contract HDTRA1-19-D-0007), University of Virginia Strategic Investment Fund award number SIF160, VDH Grant PV-BII VDH COVID-19 Modeling Program VDH-21-501-0135, NSF XSEDE Grant TG-BIO210084, National Institutes of Health (NIH) Grants 1R01GM109718, 2R01GM109718, The James S. McDonnell Foundation twenty-first Century Science Initiative Collaborative Award in Understanding Dynamic and Multi-scale Systems, the C3.ai Digital Transformation Institute and Microsoft Corporation, Gift from Google, LLC, and the National Science Foundation (NSF) grants IIS-1633028 (BIG DATA), CMMI-1745207 (EAGER), OAC-1916805 (CINES), CCF-1918656 (Expeditions), CNS-2028004 (RAPID), OAC-2027541 (RAPID), CCF-2142997 (RAPID), CNS-2041952 (PREPARE), IIS-1908530, IIS-1955797, IIS-2027848, CNS-2027908 and CCF1917819. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon.
Editors and Affiliations
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Adiga, A. et al. (2022). AI Techniques for Forecasting Epidemic Dynamics: Theory and Practice. In: Lidströmer, N., Eldar, Y.C. (eds) Artificial Intelligence in Covid-19. Springer, Cham. https://doi.org/10.1007/978-3-031-08506-2_9
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-08505-5
Online ISBN: 978-3-031-08506-2