The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
A New Framework and Software to Estimate Time-Varying Reproduction Numbers During Epidemics
Open accessAbstract
The quantification of transmissibility during epidemics is essential to designing and adjusting public health responses. Transmissibility can be measured by the reproduction number R, the average number of secondary cases caused by an infected individual. Several methods have been proposed to estimate R over the course of an epidemic; however, they are usually difficult to implement for people without a strong background in statistical modeling. Here, we present a ready-to-use tool for estimating R from incidence time series, which is implemented in popular software including Microsoft Excel (Microsoft Corporation, Redmond, Washington). This tool produces novel, statistically robust analytical estimates of R and incorporates uncertainty in the distribution of the serial interval (the time between the onset of symptoms in a primary case and the onset of symptoms in secondary cases). We applied the method to 5 historical outbreaks; the resulting estimates of R are consistent with those presented in the literature. This tool should help epidemiologists quantify temporal changes in the transmission intensity of future epidemics by using surveillance data.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Infectious Diseases of Humans: Dynamics and Control
(1992) · Annals of Internal Medicine · doi:10.7326/0003-4819-117-2-174_4 · Cited by 7,819
Infectious Diseases of Humans: Dynamics and Control
Anderson, R. M., & May, R. M. (1991) · doi:10.1093/oso/9780198545996.001.0001 · Cited by 6,179
A Major Outbreak of Severe Acute Respiratory Syndrome in Hong Kong
Lee, N. L. S., Hui, D. S. C., Wu, A. K. L., Chan, P. K. S., Cameron, P. A., Joynt, G. M., Ahuja, A. T., Yung, M. Y., Leung, C. B., To, K. F., et al. (2003) · New England Journal of Medicine · doi:10.1056/nejmoa030685 · Cited by 2,638
Peiris, J., Chu, C., Cheng, V., Chan, K., Hung, I., Poon, L., Law, K., Tang, B., Hon, T., Chan, C., et al. (2003) · The Lancet · doi:10.1016/s0140-6736(03)13412-5 · Cited by 2,375
Strategies for mitigating an influenza pandemic
Ferguson, N. M., Cummings, D. A. T., Fraser, C., Cajka, J. C., Cooley, P. C., & Burke, D. S. (2006) · Nature · doi:10.1038/nature04795 · Cited by 2,203
Self-citationPandemic Potential of a Strain of Influenza A (H1N1): Early Findings
Fraser, C., Donnelly, C. A., Cauchemez, S., Hanage, W. P., Kerkhove, M. D. V., Hollingsworth, T. D., Griffin, J. T., Baggaley, R. F., Jenkins, H. E., Lyons, E., et al. (2009) · Science · doi:10.1126/science.1176062 · Cited by 1,915
Self-citationStrategies for containing an emerging influenza pandemic in Southeast Asia
Ferguson, N. M., Cummings, D. A. T., Cauchemez, S., Fraser, C., Riley, S., Meeyai, A. C., Iamsirithaworn, S., & Burke, D. S. (2005) · Nature · doi:10.1038/nature04017 · Cited by 1,820
Self-citation(1988) · World Health Organization eBooks · Cited by 1,574
Transmission Dynamics and Control of Severe Acute Respiratory Syndrome
Lipsitch, M., Cohen, T., Cooper, B. S., Robins, J. M., Ma, S., James, L., Gopalakrishna, G., Chew, S. K., Tan, C. C., Samore, M. H., et al. (2003) · Science · doi:10.1126/science.1086616 · Cited by 1,485
Elwood, J. M. (1989) · Journal of Epidemiology & Community Health · doi:10.1136/jech.43.1.92 · Cited by 1,412
Cited by
Sorted by citations
Items 301–400 of 999
Ogi-Gittins, I., Hart, W. S., Song, J., Nash, R. K., Polonsky, J. A., Cori, A., Hill, E. M., & Thompson, R. N. (2024) · Epidemics · doi:10.1016/j.epidem.2024.100773 · Cited by 24
Self-citationReal-Time Assessment of Health-Care Requirements During the Zika Virus Epidemic in Martinique
Andronico, A., Dorléans, F., Fergé, J.-L., Salje, H., Ghawche, F., Signaté, A., Daudens‐Vaysse, E., Baudouin, L., Dub, T., Aubry, M., et al. (2017) · American Journal of Epidemiology · doi:10.1093/aje/kwx008 · Cited by 24
Self-citationMeteorological drivers of respiratory syncytial virus infections in Singapore
Ali, S. T., Tam, C. C., Cowling, B. J., Yeo, K. T., & Yung, C. F. (2020) · Scientific Reports · doi:10.1038/s41598-020-76888-4 · Cited by 24
Jordan, A., Sadler, R. J., Sawford, K., van Andel, M., Ward, M. P., & Cowled, B. (2020) · Transboundary and Emerging Diseases · doi:10.1111/tbed.13941 · Cited by 26
The COVID-19 outbreak in Sichuan, China: Epidemiology and impact of interventions
Liu, Q.-H., Bento, A. I., Yang, K., Zhang, H., Yang, X., Merler, S., Vespignani, A., Lv, J., Yu, H., Zhang, W., et al. (2020) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1008467 · Cited by 25
Dainton, C. J., & Hay, A. (2021) · BMC Public Health · doi:10.1186/s12889-021-11684-x · Cited by 24
Early detection of COVID-19 outbreaks using human mobility data
Guan, G., Dery, Y., Yechezkel, M., Ben‐Gal, I., Yamin, D., & Brandeau, M. L. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253865 · Cited by 24
Francisco, R. D. S., Lamarca, A. P., de Almeida, L. G. P., Cavalcante, L. T. D. F., Machado, D. T., Martins, Y., Brustolini, O. J. B., Gerber, A. L., Guimarães, A. P. D. C., Bellini, R. G., et al. (2021) · Viruses · doi:10.3390/v13102013 · Cited by 26
Overlapping timescales obscure early warning signals of the second COVID-19 wave
Dablander, F., Heesterbeek, H., Borsboom, D., & Drake, J. M. (2022) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2021.1809 · Cited by 24
Transmission Dynamics of COVID-19 in Ghana and the Impact of Public Health Interventions
Ofori, S. K., Schwind, J. S., Sullivan, K. L., Cowling, B. J., Chowell, G., & Fung, I. C. (2022) · American Journal of Tropical Medicine and Hygiene · doi:10.4269/ajtmh.21-0718 · Cited by 24
Chan, T., Chou, C., Chu, Y., Tang, J.-H., Chen, L.-C., Lin, H.-H., Chen, K. J., & Chen, R. (2022) · Scientific Reports · doi:10.1038/s41598-022-16011-x · Cited by 24
Kiem, C. T., & Bedford, T. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2305299121 · Cited by 20
Impact of small-area lockdowns for the control of the COVID-19 pandemicPreprint
Cuadrado, C., Monsalves, M. J., Jean Gajardo, J., Bertoglia, M. P., Nájera, M., Alfaro, T., Canals, M., Kaufman, J. S., & Peña, S. (2020) · medRxiv · doi:10.1101/2020.05.05.20092106 · Cited by 23
Gozzi, N., Chinazzi, M., Davis, J. T., Mu, K., y Piontti, A. P., Vespignani, A., & Perra, N. (2022) · medRxiv · doi:10.1101/2022.01.04.22268721 · Cited by 24
Gastañaduy, P. A., Funk, S., Paul, P., Tatham, L. M., Fisher, N., Budd, J., Fowler, B., de Fijter, S., DiOrio, M., Wallace, G. S., et al. (2018) · American Journal of Epidemiology · doi:10.1093/aje/kwy082 · Cited by 23
Impact of nonpharmacological interventions on COVID-19 transmission dynamics in India
Dikid, T., Patel, P., Athotra, A., Vaisakh, T. P., Jain, S., & NCDC COVID Incident Management Team (2020) · Indian Journal of Public Health · doi:10.4103/ijph.ijph_510_20 · Cited by 23
Novel approach for Monte Carlo simulation of the new COVID-19 spread dynamics
Maltezos, S., & Georgakopoulou, A. (2021) · Infection Genetics and Evolution · doi:10.1016/j.meegid.2021.104896 · Cited by 23
Leveraging of SARS-CoV-2 PCR Cycle Thresholds Values to Forecast COVID-19 Trends
Yin, N., Dellicour, S., Daubie, V., Franco, N., Wautier, M., Faes, C., Cauteren, D. V., Nymark, L. S., Hens, N., Gilbert, M., et al. (2021) · Frontiers in Medicine · doi:10.3389/fmed.2021.743988 · Cited by 23
Evolution of Responses to COVID-19 and Epidemiological Characteristics in South Korea
Jeon, J., Han, C. Y., Kim, T., & Lee, S. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19074056 · Cited by 23
Schrarstzhaupt, I. N., Fontes‐Dutra, M., & Díaz-Quijano, F. A. (2022) · Travel Medicine and Infectious Disease · doi:10.1016/j.tmaid.2022.102484 · Cited by 23
Storvik, G. O., Palomares, A. D.-L., Engebretsen, S., Rø, G., Engø‐Monsen, K., Kristoffersen, A. B., de Blasio, B. F., & Frigessi, A. (2023) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1093/jrsssa/qnad043 · Cited by 27
Cintia, P., Fadda, D., Giannotti, F., Pappalardo, L., Rossetti, G., Pedreschi, D., Rinzivillo, S., Bonato, P., Fabbri, F., Penone, F., et al. (2020) · arXiv (Cornell University) · Cited by 23
Wang, K., Zhao, S., Li, H., Song, Y., Wang, L., Wang, M. H., Peng, Z., Li, H., & He, D. (2020) · Annals of Translational Medicine · doi:10.21037/atm-20-1944 · Cited by 22
What can we learn from COVID-19 data by using epidemic models with unidentified infectious cases?
Griette, Q., Université de Bordeaux, I., Jacques Demongeot, A., Magal, P., & Université Grenoble Alpes, A. E. (2021) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2022025 · Cited by 22
Adaptive Estimation for Epidemic Renewal and Phylogenetic Skyline Models
Parag, K. V., & Donnelly, C. A. (2020) · Systematic Biology · doi:10.1093/sysbio/syaa035 · Cited by 22
Modeling and forecasting the COVID‐19 pandemic time‐series data
Doornik, J. A., Castle, J. L., & Hendry, D. F. (2021) · Social Science Quarterly · doi:10.1111/ssqu.13008 · Cited by 22
Quantifying the impact of US state non-pharmaceutical interventions on COVID-19 transmissionPreprint
Korevaar, H., Becker, A. D., Miller, I. F., Grenfell, B. T., Metcalf, C. J. E., & Mina, M. J. (2020) · medRxiv · doi:10.1101/2020.06.30.20142877 · Cited by 23
Suppressing COVID-19 Transmission in Hong Kong: An Observational Study of the First Four MonthsPreprint
Wu, P., Tsang, T. K., Wong, J. Y., Ng, T. W. Y., Ho, F. C. S., Gao, H., Adam, D. C., Cheung, D. H., Lau, E. H. Y., Lim, W. W., et al. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3627304 · Cited by 22
Effects of meteorological factors on influenza transmissibility by virus type/subtype
Yan, Z.-L., Liu, W., Long, Y., Ming, B.-W., Zhou, Y., Qin, P., Ou, C., & Li, L. (2024) · BMC Public Health · doi:10.1186/s12889-024-17961-9 · Cited by 21
Rieper, F., Röhl, C. H. F., & Micheli, E. D. (2020) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-020-00608-0 · Cited by 21
Importations of COVID-19 into African countries and risk of onward spread
Sun, H., Dickens, B. S. L., Cook, A. R., & Clapham, H. (2020) · BMC Infectious Diseases · doi:10.1186/s12879-020-05323-w · Cited by 21
Durmuş, H., Gökler, M. E., & Metintaş, S. (2020) · Journal of Preventive Medicine and Public Health · doi:10.3961/jpmph.20.381 · Cited by 21
Alves, G., de Lima, L. C., da Silva, I. M. D., Ribeiro-Dantas, M. D. C., Monteiro, K. H. D. C., & Endo, P. T. (2021) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi10030121 · Cited by 21
Du, H., Zhu, J., Chen, J., Zhou, H., Yang, J., & Wan, H. (2021) · Aging · doi:10.18632/aging.203098 · Cited by 21
Urban-Rural Disparities for COVID-19: Evidence from 10 Countries and Areas in the Western Pacific
Park, M., Lim, J. T., Wang, L., Cook, A. R., & Dickens, B. S. L. (2021) · Health Data Science · doi:10.34133/2021/9790275 · Cited by 20
Li, R., Metcalf, C. J. E., Stenseth, N. C., & Bjørnstad, O. N. (2021) · Science Advances · doi:10.1126/sciadv.abf9040 · Cited by 18
Impact of data accuracy on the evaluation of COVID-19 mitigation policies
Starnini, M., Aleta, A., Tizzoni, M., & Moreno, Y. (2021) · Data & Policy · doi:10.1017/dap.2021.25 · Cited by 20
Lison, A., Persson, J., Banholzer, N., & Feuerriegel, S. (2022) · Eurosurveillance · doi:10.2807/1560-7917.es.2022.27.10.2100374 · Cited by 21
Inferring the reproduction number using the renewal equation in heterogeneous epidemics
Green, W. D., Ferguson, N. M., & Cori, A. (2022) · Journal of The Royal Society Interface · doi:10.1098/rsif.2021.0429 · Cited by 24
Self-citationForecasting COVID-19 Epidemic Trends by Combining a Neural Network with Rt Estimation
Cinaglia, P., & Cannataro, M. (2022) · Entropy · doi:10.3390/e24070929 · Cited by 23
The Impact of Urbanization and Human Mobility on Seasonal Influenza in Northern China
Yang, J., Guo, X., Zhang, T., Wang, Q., Zhang, X., Yang, J. H., Lai, S., Feng, L., & Yang, W. (2022) · Viruses · doi:10.3390/v14112563 · Cited by 21
Indoor relative humidity shapes influenza seasonality in temperate and subtropical climates in China
Lei, H., Yang, M., Dong, Z., Hu, K., Chen, T., Yang, L., Zhang, N., Duan, X., Yang, S., Wang, D., et al. (2022) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2022.11.023 · Cited by 21
Thompson, R. N., Lovell-Read, F. A., & Obolski, U. (2020) · Journal of Clinical Medicine · doi:10.3390/jcm9051297 · Cited by 20
Marschner, I. C. (2020) · Epidemiology and Infection · doi:10.1017/s0950268820001065 · Cited by 21
Shapiro, M. B., Karim, F., Muscioni, G., & Augustine, A. S. (2021) · Journal of Medical Internet Research · doi:10.2196/24389 · Cited by 21
Epidemic and control of COVID-19 in Niger: quantitative analyses in a least developed country
Tchole, A. I. M., Li, Z., Wei, J.-T., Ye, R., Wang, W., Du, W.-Y., Wang, H.-T., Yin, C.-N., Ji, X., Xue, F., et al. (2020) · Journal of Global Health · doi:10.7189/jogh.10.020513 · Cited by 20
Transmission dynamics and forecasts of the COVID-19 pandemic in Mexico, March-December 2020
Tariq, A., Banda, J. M., Skums, P., Dahal, S., Castillo-Garsow, C. W., Espinoza, B., Brizuela, N. G., Saenz, R. A., Kirpich, A., Luo, R., et al. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254826 · Cited by 20
Akhmetzhanov, A. R., Cheng, H., Linton, N. M., Ponce, L. J., Jian, S.-W., & Lin, H.-H. (2022) · Emerging infectious diseases · doi:10.3201/eid2810.220456 · Cited by 21
Inward, R. P. D., Parag, K. V., & Faria, N. R. (2022) · Nature Communications · doi:10.1038/s41467-022-32812-0 · Cited by 20
Disease momentum: Estimating the reproduction number in the presence of superspreading
Johnson, K. D., Beiglböck, M., Eder, M., Grass, A., Hermisson, J., Pammer, G., Polechová, J., Toneian, D., & Wölfl, B. (2021) · Infectious Disease Modelling · doi:10.1016/j.idm.2021.03.006 · Cited by 19
Bonifazi, G., Lista, L., Menasce, D., Mezzetto, M., Педрини, Д., Spighi, R., & Zoccoli, A. (2021) · The European Physical Journal Plus · doi:10.1140/epjp/s13360-021-01339-6 · Cited by 20
Spatiotemporal analysis of COVID-19 outbreaks in Wuhan, China
Liu, W., Wang, D., Hua, S., Xie, C. H., Wang, B., Qiu, W., Xu, T., Ye, Z., Yu, L., Yang, M., et al. (2021) · Scientific Reports · doi:10.1038/s41598-021-93020-2 · Cited by 21
Quach, H., Nguyen, K. C., Hoang, T. N. A., Thái, P. Q., Tran, D. N., Le, M. T. Q., Do, T. H., Vien, C. C., Lan, P. T., Ngu, N. D., et al. (2021) · International Journal of Infectious Diseases · doi:10.1016/j.ijid.2021.07.044 · Cited by 20
Yuan, P., Li, J., Aruffo, E., Li, Q., Zheng, T., Ogden, N. H., Sander, B., Heffernan, J. M., Gatov, E., Gournis, E., et al. (2022) · CMAJ Open · doi:10.9778/cmajo.20200242 · Cited by 20
Evaluating the impact of non-pharmaceutical interventions for SARS-CoV-2 on a global scalePreprint
Esra, R. T., Jamieson, L., Fox, M. P., Letswalo, D., Ngcobo, N., Mngadi-Ncube, S., Estill, J. A. M., Meyer‐Rath, G., & Keiser, O. (2020) · medRxiv · doi:10.1101/2020.07.30.20164939 · Cited by 20
Zhu, H., Chen, S., Qin, W., Joldosh, A., Chen, Y., Wang, X., Chen, K., Xie, Z., Li, L., Liu, Y., et al. (2024) · BMC Infectious Diseases · doi:10.1186/s12879-024-09750-x · Cited by 19
COVID19-Tracker: una aplicación Shiny para analizar datos de la epidemia de SARS-CoV-2 en España
Valls, J., Tobı́as, A., Satorra, P., & Tebé, C. (2020) · Gaceta Sanitaria · doi:10.1016/j.gaceta.2020.04.002 · Cited by 19
Santana-Cibrian, M., 1 Instituto de Matemáticas UNAM-Juriquilla, J., Acuña‐Zegarra, M. A., Velasco‐Hernández, J. X., 2 CONACyT - Instituto de Matemáticas, U.-J., 3 Departamento de Matemáticas, U. D. S., & 4 Nodo Multidisciplinario de Matemáticas Aplicadas, I. D. M. U.-J. (2020) · Mathematical Biosciences & Engineering · doi:10.3934/mbe.2020330 · Cited by 21
Djaafara, B. A., Whittaker, C., Watson, O. J., Verity, R., Brazeau, N. F., WİDYASTUTİ, W., Oktavia, D., Adrian, V., Salama, N., Bhatia, S., et al. (2021) · BMC Medicine · doi:10.1186/s12916-021-02016-2 · Cited by 20
Daboin, B. E. G., Bezerra, Í. M. P., Morais, T. C., Portugal, I., Echeimberg, J. D. O., CESAR, A. E. M., Cavalcanti, M. P. E., Jacintho, L. C., Raimundo, R. D., Elmusharaf, K., et al. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19031153 · Cited by 19
COVID-19 in Japan, January–March 2020: insights from the first three months of the epidemic
Imai, N., Gaythorpe, K. A. M., Bhatia, S., Mangal, T. D., Cuomo-Dannenburg, G., Unwin, H. J. T., Jauneikaite, E., & Ferguson, N. M. (2022) · BMC Infectious Diseases · doi:10.1186/s12879-022-07469-1 · Cited by 20
Self-citationEstimates of early outbreak-specific SARS-CoV-2 epidemiological parameters from genomic data
Vaughan, T. G., Scire, J., Nadeau, S., & Stadler, T. (2024) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2308125121 · Cited by 19
Inference of epidemic dynamics in the COVID-19 era and beyond
Cori, A., & Kucharski, A. J. (2024) · Epidemics · doi:10.1016/j.epidem.2024.100784 · Cited by 18
Self-citationData-driven mathematical modeling approaches for COVID-19: A survey
Jacques Demongeot, A., & Magal, P. (2024) · Physics of Life Reviews · doi:10.1016/j.plrev.2024.08.004 · Cited by 22
Otomaru, H., Kamigaki, T., Tamaki, R., Okamoto, M., Alday, P. P., Tan, A. G., Manalo, J. I. G., Segubre‐Mercado, E., Inobaya, M. T., Tallo, V. L., et al. (2019) · Open Forum Infectious Diseases · doi:10.1093/ofid/ofz045 · Cited by 19
FOUR STYLIZED FACTS ABOUT COVID‐19
Atkeson, A. G., Kopecky, K., & Zha, T. A. (2020) · International Economic Review · doi:10.1111/iere.12660 · Cited by 18
A multi-method approach to modeling COVID-19 disease dynamics in the United States
Mokhtari, A. M., Mineo, C., Kriseman, J., Kremer, P., Neal, L., & Larson, J. (2021) · Scientific Reports · doi:10.1038/s41598-021-92000-w · Cited by 19
Individual risk-aversion responses tune epidemics to critical transmissibility ( R = 1)
Manrubia, S. C., & Zanette, D. H. (2022) · Royal Society Open Science · doi:10.1098/rsos.211667 · Cited by 19
Tsang, T. K., Wu, P., Lau, E. H. Y., & Cowling, B. J. (2021) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiab299 · Cited by 19
Guerstein, S., Romeo-Aznar, V., Dekel, M., Miron, O., Davidovitch, N., Puzis, R., & Pilosof, S. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009319 · Cited by 22
Karthick, K., & Sekar, K. (2020) · Epidemiology and Health · doi:10.4178/epih.e2020028 · Cited by 18
Meta-Analysis of Several Epidemic Characteristics of COVID-19
Zhang, P., Wang, T., & Xie, S. X. (2021) · Journal of Data Science · doi:10.6339/jds.202007_18(3).0019 · Cited by 18
Martínez, Ó. F., Aguilar-Becerra, C. D., Olivares, O. C., Sánchez-Rivera, G., Hafsi, A., Tax, A. F. C., & Yam, W. D. U. (2020) · Sustainability · doi:10.3390/su12239925 · Cited by 18
Model design for nonparametric phylodynamic inference and applications to pathogen surveillance
Didelot, X., Franceschi, V. B., Frost, S. D. W., Dennis, A. M., & Volz, E. (2023) · Virus Evolution · doi:10.1093/ve/vead028 · Cited by 18
Logrosa, G., Mata, M. A., Lachica, Z. P., Estaña, L. M., & Hassall, M. E. (2021) · Risk Analysis · doi:10.1111/risa.13779 · Cited by 18
Global COVID-19 Pandemic Waves: Limited Lessons Learned Worldwide over the Past Year
Wei, Y., Guan, J., Ning, X., Li, Y., Wei, L., Shen, S., Zhang, R., Zhao, Y. C., Shen, H., & Chen, F. (2021) · Engineering · doi:10.1016/j.eng.2021.07.015 · Cited by 18
Spatially explicit effective reproduction numbers from incidence and mobility data
Trevisin, C., Bertuzzo, E., Pasetto, D., Mari, L., Miccoli, S., Casagrandi, R., Gatto, M., & Rinaldo, A. (2023) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2219816120 · Cited by 18
Lison, A., Abbott, S., Huisman, J. S., & Stadler, T. (2024) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1012021 · Cited by 18
Chan, Y.-W. D., Flasche, S., Lam, T.-L. T., Leung, M.-H. J., Miu-ling, W., Lam, H. Y., & Chuang, S. K. (2020) · Wellcome Open Research · doi:10.12688/wellcomeopenres.15896.1 · Cited by 18
A Bayesian approach for monitoring epidemics in presence of undetected cases
Andrea De Simone, A., & Piangerelli, M. (2020) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2020.110167 · Cited by 18
Implications of increasing Atlantic influence for Arctic microbial community structure
Carter-Gates, M., Balestreri, C., Thorpe, S. E., Cottier, F. R., Baylay, A. J., Bibby, T. S., Moore, C. M., & Schroeder, D. C. (2020) · Scientific Reports · doi:10.1038/s41598-020-76293-x · Cited by 22
Local measures enable COVID-19 containment with fewer restrictions due to cooperative effects
Bittihn, P., Hupe, L., Isensee, J., & Golestanian, R. (2021) · EClinicalMedicine · doi:10.1016/j.eclinm.2020.100718 · Cited by 17
Public Compliance With Social Distancing Measures and SARS-CoV-2 Spread
Liu, H., Chen, C., Cruz‐Cano, R., Guida, J., & Lee, M. (2021) · Public Health Reports · doi:10.1177/00333549211011254 · Cited by 18
The epidemicity index of recurrent SARS-CoV-2 infections
Mari, L., Casagrandi, R., Bertuzzo, E., Pasetto, D., Miccoli, S., Rinaldo, A., & Gatto, M. (2021) · Nature Communications · doi:10.1038/s41467-021-22878-7 · Cited by 17
Challen, R., Tsaneva-Atanasova, K. T., Pitt, M., Edwards, T., Gompels, L. L., Lacasa, L., Brooks‐Pollock, E., Martin, C., Griffith, G., & Danon, L. (2021) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.2020.0280 · Cited by 18
Sun, J., Zheng, Y., Liang, W., Yang, Z., Zeng, Z., Li, T., Luo, J., Ng, M. T. A., He, J., & Zhong, N. (2021) · Value in Health · doi:10.1016/j.jval.2021.10.007 · Cited by 19
Epidemiological dynamics of an urban Dengue 4 outbreak in São Paulo, Brazil
Villabona‐Arenas, C. J., de Oliveira, J. L., de Sousa-Capra, C., Balarini, K., da Fonseca, C. R. T. P., & Zanotto, P. M. D. A. (2016) · PeerJ · doi:10.7717/peerj.1892 · Cited by 21
Monitoring the spread of COVID-19 by estimating reproduction numbers over timePreprint
Hotz, T., Glock, M., Heyder, S., Semper, S., Böhle, A., & Krämer, A. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2004.08557 · Cited by 18
Ghose, A., Bhattacharya, S., Karthikeyan, A. S., Kudale, A., Monteiro, J. M., Joshi, A., Medigeshi, G. R., Kang, G., Bal, V., Rath, S., et al. (2020) · medRxiv · doi:10.1101/2020.11.17.20228155 · Cited by 18
No evidence of association between schools and SARS-CoV-2 second wave in ItalyPreprint
Gandini, S., Rainisio, M., Iannuzzo, M. L., Bellerba, F., Cecconi, F., & Scorrano, L. (2020) · medRxiv · doi:10.1101/2020.12.16.20248134 · Cited by 18
Estimating epidemiological delay distributions for infectious diseasesPreprint
Park, S. W., Akhmetzhanov, A. R., Charniga, K., Cori, A., Davies, N. G., Dushoff, J. G., Funk, S., Gostic, K. M., Grenfell, B. T., Linton, N. M., et al. (2024) · medRxiv · doi:10.1101/2024.01.12.24301247 · Cited by 18
Self-citationThe 2023 dengue outbreak in Lombardy, Italy: A one-health perspective
Rovida, F., Faccini, M., Grané, C. M., Cassaniti, I., Senatore, S., Rossetti, E., Scardina, G., Piazza, M., Campanini, G., Lilleri, D., et al. (2025) · Travel Medicine and Infectious Disease · doi:10.1016/j.tmaid.2025.102795 · Cited by 17
Peng, Z., Song, W., Ding, Z., Guan, Q., Yang, X., Xu, Q., Wang, X., & Xia, Y. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0788-3 · Cited by 17
Hukić, M., Ponjavić, M., Tahirović, E., Karabegović, A., Ferhatbegović, E., Travar, M., & Serdarević, F. (2020) · Biomolecules and Biomedicine · doi:10.17305/bjbms.2020.5081 · Cited by 17
SARS-CoV-2 Transmission Dynamics in a Sleep-Away Camp
Szablewski, C. M., Chang, K. T., McDaniel, C. J., Chu, V. T., Yousaf, A. R., Schwartz, N. G., Brown, M., Winglee, K., Paul, P., Cui, Z., et al. (2021) · PEDIATRICS · doi:10.1542/peds.2020-046524 · Cited by 17
Liu, S., Ermolieva, T. Y., Cao, G., Gong, C., & Zheng, X. (2021) · International Conference on Time Series and Forecasting (ITISE) · doi:10.3390/engproc2021005008 · Cited by 17
Davahli, M. R., Karwowski, W., & Fiok, K. (2021) · PLoS ONE · doi:10.1371/journal.pone.0253925 · Cited by 20
Cazelles, B., Champagne, C., Nguyen-Van-Yen, B., Comiskey, C. M., Vergu, E., & Roche, B. (2021) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1009211 · Cited by 17
Joint Estimation of Generation Time and Incubation Period for Coronavirus Disease 2019
Lau, Y. C., Tsang, T. K., Kennedy‐Shaffer, L., Kahn, R. J., Lau, E. H. Y., Chen, D., Wong, J. Y., Ali, S. T., Wu, P., & Cowling, B. J. (2021) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiab424 · Cited by 17
A statistical analysis for the epidemiological surveillance of COVID-19 in Chile
(2021) · Signa Vitae · doi:10.22514/sv.2021.130 · Cited by 17
Metadata
- Authors
- Cori, A., Ferguson, N. M., Fraser, C., & Cauchemez, S.
- Affiliations
- Imperial College London (GB)
- University of Oxford (GB)
- Institut Pasteur (FR)
- Corresponding author
- Cori, Anne
- Venue
- American Journal of Epidemiology
- Publisher
- Oxford University Press (OUP)
- Published
- 2013-09-15
- Volume
- 178
- Issue
- 9
- Pages
- 1505-1512
- ISSN
- 0002-9262 (ISSN-L)
- 1476-6256
- PMC
- PMC3816335
- OpenAlex
- W2097446414
- Cited by
- 1,530
- References
- 45
- Citations by year
- 2026: 94 · 2025: 141 · 2024: 161 · 2023: 199 · 2022: 268 · 2021: 328 · 2020: 262 · 2019: 21 · 2018: 16 · 2017: 16 · 2016: 16 · 2015: 3 · 2014: 4 · 2013: 1
- FWCI
- 8.33
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- American Journal of Epidemiology (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- Royal Society
- Imperial College London
- Medical Research Council (MR/K010174/1, G0800596)
- National Institute of General Medical Sciences
- Topics
- Physical Sciences › Mathematics › Modeling and Simulation › COVID-19 epidemiological studies
- Physical Sciences › Mathematics › Applied Mathematics › Mathematical and Theoretical Epidemiology and Ecology Models
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Keywords
- time-varying reproduction number, analytical estimation, transmission intensity, public health response
- MeSH terms
- Humans, Software, Time Factors, Virus Diseases, Models, Statistical, Incidence, Epidemics
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review