Producing timely, accurate, and granular estimates of public health outcomes is essential for effective public health surveillance and mitigation efforts. However, the production of these estimates is potentially delayed by reporting lags, which many times occur naturally in a data collection cycle. In this article, we review published nowcasting methods (i.e., predicting current events while accounting for reporting lags) with a focus on methods relevant for nowcasting vital statistics. Our aim is to classify the underlying modeling strategies and contexts, identify certain advantages and limitations, and present ideas for further research and development using promising statistical modeling techniques that, to the best of our knowledge, have not yet been utilized in any vital statistics nowcasting approaches.
RundleJBTurcotteDLDonnellanA, et al.Nowcasting earthquakes. Earth Spc Sci2016; 3: 480–486.
5.
GreenHKAndrewsNJBicklerG, et al.Rapid estimation of excess mortality: nowcasting during the heatwave alert in England and Wales in June 2011. J Epi Comm Health2012; 66: 866–868.
6.
NunesBNatarioICarvalhoML. Nowcasting influenza epidemics using non-homogeneous hidden markov models. Stat Med2013; 32: 2643–2660.
7.
van de KassteeleJEilersPHCWallingaJ. Nowcasting the number of new symptomatic cases during infectious disease outbreaks using constrained p-spline smoothing. Epidemiology2019; 30: 737–745.
8.
RossenLMHedegaardHWWarnerM, et al.Early provisional estimates of drug overdose, suicide, and transportation-related deaths: nowcasting methods to account for reporting lags. Vit Stat Rapid Rel, Rpt No. 011. 2021. Available from https://www.cdc.gov/nchs/data/vsrr/vsrr011-508.pdf (accessed 10 June 2025).
9.
DonkerTvan BovenMvan BallegooijenWM, et al.Nowcasting pandemic influenza A/H1N1 2009 hospitalizations in The Netherlands. Eur J Epi2011; 26: 195–201.
10.
AhmadFBAndersonRNKnightK, et al.Advancements in the national vital statistics system to meet the real-time data needs of a pandemic. Am J Public Health2021; 111: 2133–2140.
11.
LawlessJF. Adjustments for reporting delays and the prediction of occurred but not reported events. Can J Statistics1994; 22: 15–31.
12.
SumnerSAAlicALawRK, et al.Estimating national and state-level suicide deaths using a novel online symptom search data source. J Affect Disord2023; 342: 63–68.
13.
ChoiDSumnerSAHollandKM, et al.Development of a machine learning model using multiple, heterogeneous data sources to estimate weekly US suicide fatalities. JAMA Netw Open2020; 3: e2030932.
14.
PatelDSumnerSABowenD, et al. Predicting state level suicide fatalities in the United States with realtime data and machine learning. npj Men Health Res2024; 3: 3.
15.
PengYLiCRongY, et al.Real-time prediction of the daily incidence of COVID-19 in 215 countries and territories using machine learning: Model development and validation. J Med Int Res2021; 6: 1349–1360.
16.
RossenLMAhmadFBSpenserMR, et al.Methods to adjust provisional counts of drug overdose deaths for underreporting. Vit Stat Rapid Rel, Rpt No. 006. 2018. Available from https://www.cdc.gov/nchs/data/vsrr/report006.pdf (accessed 10 June 2025).
17.
GibbonsCLMangenMJJPlassD, et al.Measuring underreporting and under-ascertainment in infectious disease datasets: a comparison of methods. BMC Pub Health2014; 14: 147.
18.
MidthuneDNFayMPCleggLX, et al.Modeling report delays and reporting corrections in cancer registry data. J Am Stat Ass2005; 100: 61–70.
19.
CleggLXFeuerEJMidthuneDN, et al.Impact of reporting delay and reporting error on cancer incidence rates and trends. J Nat Cancer Inst2002; 94: 1537–1545.
20.
HöhleMan der HeidenM. Bayesian Nowcasting during the STEC O104: h4 outbreak in Germany, 2011. Biometrics2014; 70: 993–1002.
21.
BastosLSEconomouTGomesMF, et al.A modelling approach for correcting reporting delays in disease surveillance data. Stat Med2019; 38: 4363–4377.
22.
VerrallRJ. A state space representation of the chain ladder linear model. J Inst Act1989; 116: 589–609.
23.
McGoughSFJohanssonMALipsitchM, et al.Nowcasting by Bayesian smoothing: a flexible, generalizable model for real-time epidemic tracking. PLOS Comp Biol2020; 23: 1–20.
24.
SavitskyTDWilliamsMR. Bayesian Dependent functional mixture estimation for area and time-indexed data: an application for the prediction of monthly county employment. Bay Anal2022; 17: 791–815.
25.
HughesJHaranM. Dimension reduction and alleviation of confounding for spatial generalized linear mixed models. J Royal Stat Soc Ser B (Stat Meth)2013; 75: 139–159.
26.
RenYFoxEBBruceA. Clustering correlated, sparse data streams to estimate a localized housing price index. Annals App Stat2017; 11: 808–839.
27.
VerrallRJ. Statistical methods for the chain ladder technique. Cas Act Soc Forum1994; I & II: 393–446.
28.
GüntherFBenderAKatzK, et al.Nowcasting the COVID-19 pandemic in Bavaria. Biom J2020; 63: 490–502.
29.
StonerOEconomouT. Multivariate hierarchical frameworks for modeling delayed reporting in count data. Biometrics2020; 76: 789–798.
30.
de ValpinePTurekDPaciorekCJ, et al.Programming with models: writing statistical algorithms for general model structures with NIMBLE. J Comp Graph Stat2017; 26: 403–413.
31.
Gómez-RubioV. Bayesian inference with INLA. Boca Raton: CRC Press, 2021.
32.
StonerOEconomouTHallidayA. A powerful modelling framework for nowcasting and forecasting COVID-19 and other diseases. arXiv:1912.05965. 2020. Available from: https://arxiv.org/abs/1912.05965v2 (accessed 2 September 2024).
33.
BergströmFGüntherFHöhleM, et al.Bayesian nowcasting with leading indicators applied to COVID-19 fatalities in Sweden. PLOS Comp Biol2022; 33: 1–17.
34.
VerrallRJ. Chain ladder and maximum likelihood. J Inst Act1991; 118: 489–499.
35.
EnglandPDVerrallRJ. Stochastic claims reserving in general insurance. Brit Act J2002; 8: 443–518.
36.
NoufailyAGhebremichael-WeldeselassieYEnkiDG, et al.Modelling reporting delays for outbreak detection in infectious disease data. J Roy Stat Soc Ser A (Stat Soc)2015; 178: 205–222.
37.
NoufailyAEnkiDGFarringtonP, et al.An improved algorithm for outbreak detection in multiple surveillance systems. Stat Med2012; 32: 1206–1222.
38.
SalmonMSchumacherDStarkK, et al.Bayesian Outbreak detection in the presence of reporting delays. Biom J2015; 57: 1051–1067.
39.
CsörgöMHorváthL. Limit theorems in change-point analysis. New York: Wiley, 1997.
40.
ClemenRTWinklerRL. Combining probability distributions from experts in risk analysis. Risk Anal1999; 19: 187–203.
41.
FragosoTMBertoliWLouzadaF. Bayesian Model averaging: a systematic review and conceptual classification. Int Stat Rev2018; 86: 1–28.
42.
WolfframDAbbottSan der HeidenM, et al.Collaborative nowcasting of COVID-19 hospitalization incidences in Germany. PLOS Comp Biol2023; 42: 1–25.
43.
BreimanL. Random forests. Mach Learn2001; 45: 5–32.
44.
ReichNGMcGowanCJYamanaTK, et al.Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. PLOS Comp Biol2019; 44: 1–19.
45.
McAndrewTReichNG. Adaptively stacking ensembles for influenza forecasting. Stat Med2021; 40: 6931–6952.
46.
HoeghAMaitiDLemanS. Multiset model selection. J Comp Graph Stat2018; 27: 436–448.
Supplementary Material
Please find the following supplemental material available below.
For Open Access articles published under a Creative Commons License, all supplemental material carries the same license as the article it is associated with.
For non-Open Access articles published, all supplemental material carries a non-exclusive license, and permission requests for re-use of supplemental material or any part of supplemental material shall be sent directly to the copyright owner as specified in the copyright notice associated with the article.
