Berger, E., Winkelmann, J., Eckhardt, H., Nimptsch, U., Panteli, D., Reichebner, C., Rombey, T., & Busse, R. (2022). A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe. *Health Policy*, 126(5), 373–381.

Bogoch, II, Watts, A., Thomas-Bachli, A., Huber, C., Kraemer, M. U. G., & Khan, K. (2020). Pneumonia of unknown aetiology in Wuhan, China: Potential for international spread via commercial air travel. *Journal of Travel Medicine*, 27(2), Article taaa008.

Borchering, R. K., Healy, J. M., Cadwell, B. L., Johansson, M. A., Slayton, R. B., Wallace, M., & Biggerstaff, M. (2023). Public health impact of the U.S. Scenario modeling hub. *Epidemics*, 44, Article 100705.

Cacciapaglia, G., Cot, C., De Hoffer, A., Hohenegger, S., Sannino, F., & Vatani, S. (2022). Epidemiological theory of virus variants. *Physica A: Statistical Mechanics and its Applications*, 596, Article 127071.

Charmet, T., Schaeffer, L., Grant, R., Galmiche, S., Chény, O., Von Platen, C., Maurizot, A., Rogoff, A., Omar, F., David, C., Septfons, A., Cauchemez, S., Gaynard, A., Lina, B., Lefrancos, L. H., Enouf, V., Van Der Werf, S., Mailles, A., Levy-Bruhl, D., Carrat, F., & Fontanet, A. (2021). Impact of original, B.1.1.7, and B.1.351/P1 SARS-CoV-2 lineages on vaccine effectiveness of two doses of COVID-19 mRNA vaccines: Results from a nationwide case-control study in France. *The Lancet Regional Health–Europe*, 8, Article 100171.

Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore, Y., Piontti, A., Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M. E., Longini, I. M., & Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. *Science*, 368(6489), 395–400.

Cleveland, R. B., Cleveland, W. S., & Irma, T. (1990). STL: A seasonal-trend decomposition procedure based on loess. *Journal of Official Statistics*, 3–73.

Cluzel, N., Courbariaux, M., Wang, S., Moulin, L., Wurtzer, S., Bertrand, I., Laurent, K., Monfort, P., Gantzer, C., Guyader, S. L., Boni, M., Mouchel, J.-M., Maréchal, V., Nuel, G., & Maday, Y. (2022). A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater. *Environment International*, 158, Article 106998.

Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A. (2007). Predictability and epidemic pathways in global outbreaks of infectious diseases: The SARS case study. *BMC Medicine*, 5(1), 34.

COVID-19 guidance for hospital reporting and FAQs for hospitals, hospital laboratory, and acute care facility data reporting. (2023). www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf.

COVID-19 reported patient impact and hospital capacity by state timeseries (RAW). (2023). https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh.

De Hoffer, A., Vatani, S., Cot, C., Cacciapaglia, G., Chiusano, M. L., Cimarelli, A., Conventi, F., Giannini, A., Hohenegger, S., & Sannino, F. (2022). Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19. *Scientific Reports*, 12(1), 9275.

Della Morte, M., Orlando, D., & Sannino, F. (2020). Renormalization group approach to pandemics: The COVID-19 case. *Frontiers in Physiology*, 8, 144.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-Training of deep bidirectional transformers for language understanding [Internet] *arXiv* [cited 2023 Dec 7]. Available from: http://arxiv.org/abs/1810.04805.

Dings, C., Götz, K. M., Och, K., Sihinevich, I., Werthner, Q., Smola, S., Bliem, M., Mahfoud, F., Volk, T., Kreuer, S., Rissland, J., Selzer, D., & Lehr, T. (2022). Model-based analysis of SARS-CoV-2 infections, hospitalization and outcome in Germany, the federal states and districts. *Viruses*, 14(10), 2114.

Domenech De Cellès, M., Arduin, H., Lévy-Bruhl, D., Georges, S., Souty, C., Guillemot, D., Watier, L., & Opatowski, L. (2019). Unraveling the seasonal epidemiology of pneumococcus. *Proceedings of the National Academy of Sciences*, 116(5), 1802–1807.

Fauver, J. R., Petrone, M. E., Hodcroft, E. B., Shioda, K., Ehrlich, H. Y., Watts, A. G., Vogels, C. B. F., Brito, A. F., Alpert, T., Muyombwe, A., Razeq, J., Downing, R., Cheemarla, N. R., Wyllie, A. L., Kalinich, C. C., Ott, I. M., Quick, J., Loman, N. J., Neugebauer, K. M., et al. (2020). Coast-to-Coast spread of SARS-CoV-2 during the early epidemic in the United States. *Cell*, 181(5), 990–996.e5.

Ferté, T., Jouhet, V., Griffier, R., Hejblum, B. P., Thiébaut, R., Bordeaux University Hospital Covid-19 Crisis Task Force, Faure, I., Revel, P., Tentillier, E., Dindart, J.-M., Gruson, D., Joannes-Boyau, O., Malvy, J.-M. D., Pistone, T., Neau, D., Nguyen, D., Lafon, M.-E., Molimard, M., Schaeverbeke, T., et al. (2022). The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France. *JAMIA Open*, 5(4), 00ac086.

Fields, R., Humphrey, L., Flynn-Primrose, D., Mohammadi, Z., Nahirniak, M., Thommes, E. W., & Cojocaru, M. G. (2021). Age-stratified transmission model of COVID-19 in Ontario with human mobility during pandemic's first wave. *Heliyon*, 7(9), Article e07905.

FluSight Forecast Hub. (2023). https://github.com/cddepi/Flusight-forecast-data.
Galmiche, S., Charmet, T., Schaeffer, L., Paireau, J., Grant, R., Chény, O., Von Platen, C., Maurizot, A., Blanc, C., Dinis, A., Martin, S., Omar, F., David, C., Septfons, A., Cauchemez, S., Carrat, F., Mailles, A., Levy-Bruhl, D., & Fontanet, A. (2021). Exposures associated with SARS-CoV-2 infection in France: A nationwide online case-control study. *The Lancet Regional Health–Europe*, 7, Article 100148.

Gilbert, M., Pullano, L., Pinotti, F., Valdano, E., Poletto, C., Boëlle, P.-Y., D'Ortenzio, E., Yazdanpanah, Y., Eholie, S. P., Altmann, M., Gutierrez, B., Kraemer, M. U. G., & Colizza, V. (2020). Preparedness and vulnerability of african countries against importations of COVID-19: A modelling study. *The Lancet*, 395, 871–877, 10227.

Grant, R., Charmet, T., Schaeffer, L., Galmiche, S., Madec, Y., Von Platen, C., Chény, O., Omar, F., David, C., Rogoff, A., Paireau, J., Cauchemez, S., Carrat, F., Septfons, A., Levy-Bruhl, D., Mailles, A., & Fontanet, A. (2022). Impact of SARS-CoV-2 Delta variant on incubation, transmission settings and vaccine effectiveness: Results from a nationwide case-control study in France. *The Lancet Regional Health–Europe*, 13, Article 100278.

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure [Internet] *arXiv* [cited 2023 Dec 7]. Available from: http://arxiv.org/abs/2203.05794.

Howerton, E., Contamin, L., Mullany, L. C., Qin, M., Reich, N. G., Bents, S., Borchering, R. K., Jung, S., Loo, S. L., Smith, C. P., Levander, J., Kerr, J., Espino, J., Van Panhuis, W. G., Hochheiser, H., Galanti, M., Yamana, T., Pei, S., Shaman, J., et al. (2023). Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub [Internet] *Infectious Diseases (except HIV/AIDS)* [cited 2023 Dec 1]. Available from: http://medrxiv.org/lookup/doi/10.1101/2023.06.28.23291998.

Hurford, A., Martignoni, M. M., Loredo-Osti, J. C., Anokye, F., Arino, J., Husain, B. S., Gaas, B., & Watmough, J. (2023). Pandemic modelling for regions implementing an elimination strategy. *Journal of Theoretical Biology*, 561, Article 111378.

Johansson, M. A., Apfeldorf, K. M., Dobson, S., Devita, J., Buczak, A. L., Baugher, B., Moniz, L. J., Bagley, T., Babin, S. M., Guven, E., Yamana, T. K., Shaman, J., Moschou, T., Lothian, N., Lane, A., Osborne, G., Jiang, G., Brooks, L. C., Farrow, D. C., et al. (2019). An open challenge to advance probabilistic forecasting for dengue epidemics. *Proceedings of the National Academy of Sciences*, 116(48), 24268–24274.

Kong, J. D., Akpudo, U. E., Effoduh, J. O., & Bragazzi, N. L. (2023). Leveraging responsible, explainable, and local artificial intelligence solutions for clinical public health in the global south. *Healthcare*, 11(4), 457.

Kovacevic, A., Eggo, R. M., Baguelin, M., Domenech De Cellès, M., & Opatowski, L. (2022). The impact of cocirculating pathogens on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/Coronavirus disease 2019 surveillance: How concurrent epidemics may introduce bias and decrease the observed SARS-CoV-2 percentage positivity. *The Journal of Infectious Diseases*, 225(2), 199–207.

Lefrant, J.-Y., Pirracchio, R., Benhamou, D., Dureuil, B., Pottecher, J., Samain, E., Joannes-Boyau, O., & Bouaziz, H. (2021). ICU bed capacity during COVID-19 pandemic in France: From ephemeral beds to continuous and permanent adaptation. *Anaesthesia, Critical Care & Pain Medicine*, 40(3), Article 100873.

Lundberg, S., & Lee, S. I. (2016). An unexpected unity among methods for interpreting model predictions [Internet] *arXiv* [cited 2024 Jan 17]. Available from: http://arxiv.org/abs/1611.07478.

Lutz, C. S., Huynh, M. P., Schroeder, M., Anyatonwu, S., Dahlgren, F. S., Danyluk, G., Fernandez, D., Greene, S. K., Kipshidze, N., Liu, L., Mgbere, O., McHugh, L. A., Myers, J. F., Siniscalchi, A., Sullivan, A. D., West, N., Johansson, M. A., & Biggerstaff, M. (2019). Applying infectious disease forecasting to public health: A path forward using influenza forecasting examples. *BMC Public Health*, 19(1), 1659.

Martin, L., Muller, B., Ortiz Suárez, P. J., Dupont, Y., Romary, L., De La Clergerie, É., Seddah, D., & Sagot, B. (2020). CamemBERT: A tasty French language model [cited 2023 Dec 7]. In *Proceedings of the 58th annual meeting of the association for computational linguistics [Internet]. Online: Association for computational linguistics* (pp. 7203–7219). Available from: https://www.aclweb.org/anthology/2020.acl-main.645.
Berger, E., Winkelmann, J., Eckhardt, H., Nimptsch, U., Panteli, D., Reichebner, C., Rombey, T., & Busse, R. (2022). A country-level analysis comparing hospital capacity and utilisation during the first COVID-19 wave across Europe. *Health Policy*, 126(5), 373–381.

Bogoch, II, Watts, A., Thomas-Bachli, A., Huber, C., Kraemer, M. U. G., & Khan, K. (2020). Pneumonia of unknown aetiology in Wuhan, China: Potential for international spread via commercial air travel. *Journal of Travel Medicine*, 27(2), Article taaa008.

Borchering, R. K., Healy, J. M., Cadwell, B. L., Johansson, M. A., Slayton, R. B., Wallace, M., & Biggerstaff, M. (2023). Public health impact of the U.S. Scenario modeling hub. *Epidemics*, 44, Article 100705.

Cacciapaglia, G., Cot, C., De Hoffer, A., Hohenegger, S., Sannino, F., & Vatani, S. (2022). Epidemiological theory of virus variants. *Physica A: Statistical Mechanics and its Applications*, 596, Article 127071.

Charmet, T., Schaeffer, L., Grant, R., Galmiche, S., Chény, O., Von Platen, C., Maurizot, A., Rogoff, A., Omar, F., David, C., Septfons, A., Cauchemez, S., Gaynard, A., Lina, B., Lefrancos, L. H., Enouf, V., Van Der Werf, S., Mailles, A., Levy-Bruhl, D., Carrat, F., & Fontanet, A. (2021). Impact of original, B.1.1.7, and B.1.351/P1 SARS-CoV-2 lineages on vaccine effectiveness of two doses of COVID-19 mRNA vaccines: Results from a nationwide case-control study in France. *The Lancet Regional Health–Europe*, 8, Article 100171.

Chinazzi, M., Davis, J. T., Ajelli, M., Gioannini, C., Litvinova, M., Merler, S., Pastore, Y., Piontti, A., Mu, K., Rossi, L., Sun, K., Viboud, C., Xiong, X., Yu, H., Halloran, M. E., Longini, I. M., & Vespignani, A. (2020). The effect of travel restrictions on the spread of the 2019 novel coronavirus (COVID-19) outbreak. *Science*, 368(6489), 395–400.

Cleveland, R. B., Cleveland, W. S., & Irma, T. (1990). STL: A seasonal-trend decomposition procedure based on loess. *Journal of Official Statistics*, 3–73.

Cluzel, N., Courbariaux, M., Wang, S., Moulin, L., Wurtzer, S., Bertrand, I., Laurent, K., Monfort, P., Gantzer, C., Guyader, S. L., Boni, M., Mouchel, J.-M., Maréchal, V., Nuel, G., & Maday, Y. (2022). A nationwide indicator to smooth and normalize heterogeneous SARS-CoV-2 RNA data in wastewater. *Environment International*, 158, Article 106998.

Colizza, V., Barrat, A., Barthélemy, M., & Vespignani, A. (2007). Predictability and epidemic pathways in global outbreaks of infectious diseases: The SARS case study. *BMC Medicine*, 5(1), 34.

COVID-19 guidance for hospital reporting and FAQs for hospitals, hospital laboratory, and acute care facility data reporting. (2023). www.hhs.gov/sites/default/files/covid-19-faqs-hospitals-hospital-laboratory-acute-care-facility-data-reporting.pdf.

COVID-19 reported patient impact and hospital capacity by state timeseries (RAW). (2023). https://healthdata.gov/Hospital/COVID-19-Reported-Patient-Impact-and-Hospital-Capa/g62h-syeh.

De Hoffer, A., Vatani, S., Cot, C., Cacciapaglia, G., Chiusano, M. L., Cimarelli, A., Conventi, F., Giannini, A., Hohenegger, S., & Sannino, F. (2022). Variant-driven early warning via unsupervised machine learning analysis of spike protein mutations for COVID-19. *Scientific Reports*, 12(1), 9275.

Della Morte, M., Orlando, D., & Sannino, F. (2020). Renormalization group approach to pandemics: The COVID-19 case. *Frontiers in Physiology*, 8, 144.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-Training of deep bidirectional transformers for language understanding [Internet] *arXiv* [cited 2023 Dec 7]. Available from: http://arxiv.org/abs/1810.04805.

Dings, C., Götz, K. M., Och, K., Sihinevich, I., Werthner, Q., Smola, S., Bliem, M., Mahfoud, F., Volk, T., Kreuer, S., Rissland, J., Selzer, D., & Lehr, T. (2022). Model-based analysis of SARS-CoV-2 infections, hospitalization and outcome in Germany, the federal states and districts. *Viruses*, 14(10), 2114.

Domenech De Cellès, M., Arduin, H., Lévy-Bruhl, D., Georges, S., Souty, C., Guillemot, D., Watier, L., & Opatowski, L. (2019). Unraveling the seasonal epidemiology of pneumococcus. *Proceedings of the National Academy of Sciences*, 116(5), 1802–1807.

Fauver, J. R., Petrone, M. E., Hodcroft, E. B., Shioda, K., Ehrlich, H. Y., Watts, A. G., Vogels, C. B. F., Brito, A. F., Alpert, T., Muyombwe, A., Razeq, J., Downing, R., Cheemarla, N. R., Wyllie, A. L., Kalinich, C. C., Ott, I. M., Quick, J., Loman, N. J., Neugebauer, K. M., et al. (2020). Coast-to-Coast spread of SARS-CoV-2 during the early epidemic in the United States. *Cell*, 181(5), 990–996.e5.

Ferté, T., Jouhet, V., Griffier, R., Hejblum, B. P., Thiébaut, R., Bordeaux University Hospital Covid-19 Crisis Task Force, Faure, I., Revel, P., Tentillier, E., Dindart, J.-M., Gruson, D., Joannes-Boyau, O., Malvy, J.-M. D., Pistone, T., Neau, D., Nguyen, D., Lafon, M.-E., Molimard, M., Schaeverbeke, T., et al. (2022). The benefit of augmenting open data with clinical data-warehouse EHR for forecasting SARS-CoV-2 hospitalizations in Bordeaux area, France. *JAMIA Open*, 5(4), 00ac086.

Fields, R., Humphrey, L., Flynn-Primrose, D., Mohammadi, Z., Nahirniak, M., Thommes, E. W., & Cojocaru, M. G. (2021). Age-stratified transmission model of COVID-19 in Ontario with human mobility during pandemic's first wave. *Heliyon*, 7(9), Article e07905.

FluSight Forecast Hub. (2023). https://github.com/cdcepif/Flusight-forecast-data.
Galmiche, S., Charmet, T., Schaeffer, L., Paireau, J., Grant, R., Chény, O., Von Platen, C., Maurizot, A., Blanc, C., Dinis, A., Martin, S., Omar, F., David, C., Septfons, A., Cauchemez, S., Carrat, F., Mailles, A., Levy-Bruhl, D., & Fontanet, A. (2021). Exposures associated with SARS-CoV-2 infection in France: A nationwide online case-control study. *The Lancet Regional Health–Europe*, 7, Article 100148.

Gilbert, M., Pullano, L., Pinotti, F., Valdano, E., Poletto, C., Boëlle, P.-Y., D'Ortenzio, E., Yazdanpanah, Y., Eholie, S. P., Altmann, M., Gutierrez, B., Kraemer, M. U. G., & Colizza, V. (2020). Preparedness and vulnerability of african countries against importations of COVID-19: A modelling study. *The Lancet*, 395, 871–877, 10227.

Grant, R., Charmet, T., Schaeffer, L., Galmiche, S., Madec, Y., Von Platen, C., Chény, O., Omar, F., David, C., Rogoff, A., Paireau, J., Cauchemez, S., Carrat, F., Septfons, A., Levy-Bruhl, D., Mailles, A., & Fontanet, A. (2022). Impact of SARS-CoV-2 Delta variant on incubation, transmission settings and vaccine effectiveness: Results from a nationwide case-control study in France. *The Lancet Regional Health–Europe*, 13, Article 100278.

Grootendorst, M. (2022). BERTopic: Neural topic modeling with a class-based TF-IDF procedure [Internet] *arXiv* [cited 2023 Dec 7]. Available from: http://arxiv.org/abs/2203.05794.

Howerton, E., Contamin, L., Mullany, L. C., Qin, M., Reich, N. G., Bents, S., Borchering, R. K., Jung, S., Loo, S. L., Smith, C. P., Levander, J., Kerr, J., Espino, J., Van Panhuis, W. G., Hochheiser, H., Galanti, M., Yamana, T., Pei, S., Shaman, J., et al. (2023). Informing pandemic response in the face of uncertainty. An evaluation of the U.S. COVID-19 Scenario Modeling Hub [Internet] *Infectious Diseases (except HIV/AIDS)* [cited 2023 Dec 1]. Available from: http://medrxiv.org/lookup/doi/10.1101/2023.06.28.23291998.

Hurford, A., Martignoni, M. M., Loredo-Osti, J. C., Anokye, F., Arino, J., Husain, B. S., Gaas, B., & Watmough, J. (2023). Pandemic modelling for regions implementing an elimination strategy. *Journal of Theoretical Biology*, 561, Article 111378.

Johansson, M. A., Apfeldorf, K. M., Dobson, S., Devita, J., Buczak, A. L., Baugher, B., Moniz, L. J., Bagley, T., Babin, S. M., Guven, E., Yamana, T. K., Shaman, J., Moschou, T., Lothian, N., Lane, A., Osborne, G., Jiang, G., Brooks, L. C., Farrow, D. C., et al. (2019). An open challenge to advance probabilistic forecasting for dengue epidemics. *Proceedings of the National Academy of Sciences*, 116(48), 24268–24274.

Kong, J. D., Akpudo, U. E., Effoduh, J. O., & Bragazzi, N. L. (2023). Leveraging responsible, explainable, and local artificial intelligence solutions for clinical public health in the global south. *Healthcare*, 11(4), 457.

Kovacevic, A., Eggo, R. M., Baguelin, M., Domenech De Cellès, M., & Opatowski, L. (2022). The impact of cocirculating pathogens on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2)/Coronavirus disease 2019 surveillance: How concurrent epidemics may introduce bias and decrease the observed SARS-CoV-2 percentage positivity. *The Journal of Infectious Diseases*, 225(2), 199–207.

Lefrant, J.-Y., Pirracchio, R., Benhamou, D., Dureuil, B., Pottecher, J., Samain, E., Joannes-Boyau, O., & Bouaziz, H. (2021). ICU bed capacity during COVID-19 pandemic in France: From ephemeral beds to continuous and permanent adaptation. *Anaesthesia, Critical Care & Pain Medicine*, 40(3), Article 100873.

Lundberg, S., & Lee, S. I. (2016). An unexpected unity among methods for interpreting model predictions [Internet] *arXiv* [cited 2024 Jan 17]. Available from: http://arxiv.org/abs/1611.07478.

Lutz, C. S., Huynh, M. P., Schroeder, M., Anyatonwu, S., Dahlgren, F. S., Danyluk, G., Fernandez, D., Greene, S. K., Kipshidze, N., Liu, L., Mgbere, O., McHugh, L. A., Myers, J. F., Siniscalchi, A., Sullivan, A. D., West, N., Johansson, M. A., & Biggerstaff, M. (2019). Applying infectious disease forecasting to public health: A path forward using influenza forecasting examples. *BMC Public Health*, 19(1), 1659.

Martin, L., Muller, B., Ortiz Suárez, P. J., Dupont, Y., Romary, L., De La Clergerie, È., Seddah, D., & Sagot, B. (2020). Camembert: A tasty French language model [cited 2023 Dec 7]. In *Proceedings of the 58th annual meeting of the association for computational linguistics [Internet]. Online: Association for computational linguistics* (pp. 7203–7219). Available from: https://www.aclweb.org/anthology/2020.acl-main.645.
Mathis, S. M., Webber, A. E., León, T. M., Murray, E. L., Sun, M., White, L. A., Brooks, L. C., Green, A., Hu, A. J., McDonald, D. J., Rosenfeld, R., Shemetov, D., Tibshirani, R. J., Kandula, S., Pei, S., Shaman, J., Yaari, R., Yamana, T. K., Agarwal, et al. (2023). Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations. medRxiv, 2023.000511, doi: 10.1101/2023.000511.

McGowan, C. J., Biggerstaff, M., Johansson, M., Apfeldorf, K. M., Ben-Nun, M., Brooks, L., Convertino, M., Erraguntla, M., Farrow, D. C., Freeze, J., Ghosh, S., Hyun, S., Kandula, S., Lega, J., Liu, Y., Michaud, N., Morita, H., Niemi, J., Ramakrishnan, N., et al. (2019). Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016. Scientific Reports, 9(1), 683.

Metcalf, C. J. E., Morris, D. H., & Park, S. W. (2020). Mathematical models to guide pandemic response. Science, 369(6502), 368–369.

Olsen, S. J., Winn, A. K., Budd, A. P., Prill, M. M., Steel, J., Midgley, C. M., Kniss, K., Burns, E., Rowe, T., Foust, A., Jasso, G., Merced-Morales, A., Davis, C. T., Jang, Y., Jones, J., Daly, P., Gubareva, L., Barnes, J., Kondor, et al. (2021). Changes in influenza and other respiratory virus activity during the COVID-19 pandemic — United States, 2020–2021. Morbidity and Mortality Weekly Report, 70(29), 1013–1019.

Opatowski, L., Baguelin, M., & Eggo, R. M. (2018). Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling. In T. C. Hobman (Ed.), PLOS pathog (Vol. 14), Article e1006770, 2.

Parino, F., Zino, L., Porfiri, M., & Rizzo, A. (2021). Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading. Journal of The Royal Society Interface, 18(175), Article 20200875. rsif.2020.0875.

Perez-Guzman, P. N., Knock, E., Imai, N., Rawson, T., Elmaci, Y., Alcada, J., Whittles, L. K., Thekke Kanapram, D., Sonabend, R., Gaythorpe, K. A. M., Hinsley, W., FitzJohn, R. G., Volz, E., Verity, R., Ferguson, N. M., Cori, A., & Baguelin, M. (2023). Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England. Nature Communications, 14(1), 4279.

Pinotti, F., Di Domenico, L., Ortega, E., Mancastroppa, M., Pullano, G., Valdano, E., Boëlle, P.-Y., Poletto, C., Colizza, V., Pinotti, F., Di Domenico, L., Ortega, E., Mancastroppa, M., Pullano, G., Valdano, E., et al. (2020). Tracing and analysis of 288 early SARS-CoV-2 infections out00side China: A modeling study. In R. Z. Aandahl (Ed.), PLOS med (Vol. 17), Article e1003193, 7.

Poletto, C., Gomes, M. F., Pastore, Y., Piontii, A., Rossi, L., Bioglio, L., Chao, D. L., Longini, I. M., Jr., Halloran, M. E., Colizza, V., & Vespignani. (2014). Assessing the impact of travel restrictions on international spread of the 2014 West African Ebola epidemic [Internet] [cited 2024 Jan 8] Euro Surveillance, 19(42). Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2014.19.42.20936.

Poletto, C., Pelat, C., Lévy-Bruhl, D., Yazdanpanah, Y., Boëlle, P. Y., & Colizza, V. (2014). Assessment of the Middle East respiratory syndrome coronavirus (MERS-CoV) epidemic in the Middle East and risk of international spread using a novel maximum likelihood analysis approach [Internet] [cited 2024 Jan 8] Euro Surveillance, 19(23). Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2014.19.23.20824.

Pullano, G., Pinotti, F., Valdano, E., Boëlle, P. Y., Poletto, C., & Colizza, V. (2020). Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020 [Internet] [cited 2024 Jan 8] Euro Surveillance, 25(4). Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.4.2000057.

Reich, N. G., McGowan, C. J., Yamana, T. K., Tushar, A., Ray, E. L., Osthus, D., Kandula, S., Brooks, L. C., Crawford-Crudell, W., Gibson, G. C., Moore, E., Silva, R., Biggerstaff, M., Johansson, M. A., Rosenfeld, R., & Shaman, J. (2019). Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. Pitzer VE, editor. PLoS Computational Biology, 15(11), Article e1007486.

Sonabend, R., Whittles, L. K., Imai, N., Perez-Guzman, P. N., Knock, E. S., Rawson, T., Gaythorpe, K. A. M., Djaafara, B. A., Hinsley, W., FitzJohn, R. G., Lees, J. A., Kanapram, D. T., Volz, E. M., Ghani, A. C., Ferguson, N. M., Baguelin, M., & Cori, A. (2021). Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: A mathematical modelling study. The Lancet, 398, 1825–1835, 10313.

Stevenson, F., Hayasi, K., Bragazzi, N. L., Kong, J. D., Asgary, A., Lieberman, B., Ruan, X., Mathaha, T., Dahbi, S.-E., Choma, J., Kawonga, M., Mbada, M., Tripathi, N., Orbinski, J., Mellado, B., & Wu, J. (2021). Development of an early alert system for an additional wave of COVID-19 cases using a recurrent neural network with long short-term memory. International Journal of Environmental Research and Public Health, 18(14), 7376.

U.S. Outpatient influenza-like illness surveillance network (ILINet). (2023). https://wwwnn.cdc.gov/ILIINet/.

Wang, D., Lentzen, M., Botz, J., Valderrama, D., Deplante, L., Perrio, J., Génin, M., Thommes, E., Coudeville, L., & Fröhlich, H. (2023). Development of an early alert model for pandemic situations in Germany. Scientific Reports, 13(1), Article 20780.

Weiner-Lastinger, L. M., Dudeck, M. A., Allen-Bridson, K., Dantes, R., Gross, C., Nkwata, A., Tejedor, S. C., Pollock, D., & Benin, A. (2022). Changes in the number of intensive care unit beds in US hospitals during the early months of the coronavirus disease 2019 (COVID-19) pandemic. Infection Control & Hospital Epidemiology, 43(10), 1477–1481.

Wong, A., Barrero Guevara, L. A., Goult, E., Briga, M., Kramer, S. C., Kovacevic, A., Opatowski, L., & Domenech De Celles, M. (2023). The interactions of SARS-CoV-2 with cocirculating pathogens: Epidemiological implications and current knowledge gaps. Hobman TC. PLoS Pathogens, 19(3), Article e1011167.

Wurtzer, S., Marechal, V., Mouchel, J., Maday, Y., Teyssou, R., Richard, E., Almayrac, J., & Moulin, L. (2020). Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water, Greater Paris, France, 5 March to 23 April 2020 [Internet] Euro Surveillance, 25(50) [cited 2023 Nov 30] Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.50.2000776.
Mathis, S. M., Webber, A. E., León, T. M., Murray, E. L., Sun, M., White, L. A., Brooks, L. C., Green, A., Hu, A. J., McDonald, D. J., Rosenfeld, R., Shemetov, D., Tibshirani, R. J., Kandula, S., Pei, S., Shaman, J., Yaari, R., Yamana, T. K., Agarwal, et al. (2023). Evaluation of FluSight influenza forecasting in the 2021-22 and 2022-23 seasons with a new target laboratory-confirmed influenza hospitalizations. medRxiv, 2023.000616.

McGowan, C. J., Biggerstaff, M., Johansson, M., Apfeldorf, K. M., Ben-Nun, M., Brooks, L., Convertino, M., Erraguntla, M., Farrow, D. C., Freeze, J., Ghosh, S., Hyun, S., Kandula, S., Lega, J., Liu, Y., Michaud, N., Morita, H., Niemi, J., Ramakrishnan, N., et al. (2019). Collaborative efforts to forecast seasonal influenza in the United States, 2015–2016. Scientific Reports, 9(1), 683.

Metcalf, C. J. E., Morris, D. H., & Park, S. W. (2020). Mathematical models to guide pandemic response. Science, 369(6502), 368–369.

Olsen, S. J., Winn, A. K., Budd, A. P., Prill, M. M., Steel, J., Midgley, C. M., Kniss, K., Burns, E., Rowe, T., Foust, A., Jasso, G., Merced-Morales, A., Davis, C. T., Jang, Y., Jones, J., Daly, P., Gubareva, L., Barnes, J., Kondor, et al. (2021). Changes in influenza and other respiratory virus activity during the COVID-19 pandemic — United States, 2020–2021. Morbidity and Mortality Weekly Report, 70(29), 1013–1019.

Opatowski, L., Baguelin, M., & Eggo, R. M. (2018). Influenza interaction with cocirculating pathogens and its impact on surveillance, pathogenesis, and epidemic profile: A key role for mathematical modelling. In T. C. Hobman (Ed.), PLOS pathog (Vol. 14), Article e1006770, 2.

Parino, F., Zino, L., Porfiri, M., & Rizzo, A. (2021). Modelling and predicting the effect of social distancing and travel restrictions on COVID-19 spreading. Journal of The Royal Society Interface, 18(175), Article 20200875. rsif.2020.0875.

Perez-Guzman, P. N., Knock, E., Imai, N., Rawson, T., Elmaci, Y., Alcada, J., Whittles, L. K., Thekke Kanapram, D., Sonabend, R., Gaythorpe, K. A. M., Hinsley, W., FitzJohn, R. G., Volz, E., Verity, R., Ferguson, N. M., Cori, A., & Baguelin, M. (2023). Epidemiological drivers of transmissibility and severity of SARS-CoV-2 in England. Nature Communications, 14(1), 4279.

Pinotti, F., Di Domenico, L., Ortega, E., Mancastroppa, M., Pullano, G., Valdano, E., Boëlle, P.-Y., Poletto, C., Colizza, V., Pinotti, F., Di Domenico, L., Ortega, E., Mancastroppa, M., Pullano, G., Valdano, E., et al. (2020). Tracing and analysis of 288 early SARS-CoV-2 infections out00side China: A modeling study. In R. Z. Aandahl (Ed.), PLOS med (Vol. 17), Article e1003193, 7.

Poletto, C., Gomes, M. F., Pastore, Y., Piontii, A., Rossi, L., Bioglio, L., Chao, D. L., Longini, I. M., Jr., Halloran, M. E., Colizza, V., & Vespignani. (2014). Assessing the impact of travel restrictions on international spread of the 2014 West African Ebola epidemic [Internet] [cited 2024 Jan 8] Euro Surveillance, 19(42). Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2014.19.42.20936.

Poletto, C., Pelat, C., Lévy-Bruhl, D., Yazdanpanah, Y., Boëlle, P. Y., & Colizza, V. (2014). Assessment of the Middle East respiratory syndrome coronavirus (MERS-CoV) epidemic in the Middle East and risk of international spread using a novel maximum likelihood analysis approach [Internet] [cited 2024 Jan 8] Euro Surveillance, 19(23). Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES2014.19.23.20824.

Pullano, G., Pinotti, F., Valdano, E., Boëlle, P. Y., Poletto, C., & Colizza, V. (2020). Novel coronavirus (2019-nCoV) early-stage importation risk to Europe, January 2020 [Internet] [cited 2024 Jan 8] Euro Surveillance, 25(4). Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.4.2000057.

Reich, N. G., McGowan, C. J., Yamana, T. K., Tushar, A., Ray, E. L., Osthus, D., Kandula, S., Brooks, L. C., Crawford-Crudell, W., Gibson, G. C., Moore, E., Silva, R., Biggerstaff, M., Johansson, M. A., Rosenfeld, R., & Shaman, J. (2019). Accuracy of real-time multi-model ensemble forecasts for seasonal influenza in the U.S. Pitzer VE, editor. PLoS Computational Biology, 15(11), Article e1007486.

Sonabend, R., Whittles, L. K., Imai, N., Perez-Guzman, P. N., Knock, E. S., Rawson, T., Gaythorpe, K. A. M., Djaafara, B. A., Hinsley, W., FitzJohn, R. G., Lees, J. A., Kanapram, D. T., Volz, E. M., Ghani, A. C., Ferguson, N. M., Baguelin, M., & Cori, A. (2021). Non-pharmaceutical interventions, vaccination, and the SARS-CoV-2 delta variant in England: A mathematical modelling study. The Lancet, 398, 1825–1835, 10313.

Stevenson, F., Hayasi, K., Bragazzi, N. L., Kong, J. D., Asgary, A., Lieberman, B., Ruan, X., Mathaha, T., Dahbi, S.-E., Choma, J., Kawonga, M., Mbada, M., Tripathi, N., Orbinski, J., Mellado, B., & Wu, J. (2021). Development of an early alert system for an additional wave of COVID-19 cases using a recurrent neural network with long short-term memory. International Journal of Environmental Research and Public Health, 18(14), 7376.

U.S. Outpatient influenza-like illness surveillance network (ILINet). (2023). https://wwwnn.cdc.gov/ILIINet/.

Wang, D., Lentzen, M., Botz, J., Valderrama, D., Deplante, L., Perrio, J., Génin, M., Thommes, E., Coudeville, L., & Fröhlich, H. (2023). Development of an early alert model for pandemic situations in Germany. Scientific Reports, 13(1), Article 20780.

Weiner-Lastinger, L. M., Dudeck, M. A., Allen-Bridson, K., Dantes, R., Gross, C., Nkwata, A., Tejedor, S. C., Pollock, D., & Benin, A. (2022). Changes in the number of intensive care unit beds in US hospitals during the early months of the coronavirus disease 2019 (COVID-19) pandemic. Infection Control & Hospital Epidemiology, 43(10), 1477–1481.

Wong, A., Barrero Guevara, L. A., Goult, E., Briga, M., Kramer, S. C., Kovacevic, A., Opatowski, L., & Domenech De Celles, M. (2023). The interactions of SARS-CoV-2 with cocirculating pathogens: Epidemiological implications and current knowledge gaps. Hobman TC. PLoS Pathogens, 19(3), Article e1011167.

Wurtzer, S., Marechal, V., Mouchel, J., Maday, Y., Teyssou, R., Richard, E., Almayrac, J., & Moulin, L. (2020). Evaluation of lockdown effect on SARS-CoV-2 dynamics through viral genome quantification in waste water, Greater Paris, France, 5 March to 23 April 2020 [Internet] Euro Surveillance, 25(50) [cited 2023 Nov 30] Available from: https://www.eurosurveillance.org/content/10.2807/1560-7917.ES.2020.25.50.2000776.