Fenton A. Editorial: mathematical modelling of infectious diseases. Parasitology. 2016;143:801–4.

PubMed  Google Scholar 

Siettos CI, Russo L. Mathematical modeling of infectious disease dynamics. Virulence. 2013;4:295–306.

PubMed  PubMed Central  Google Scholar 

• Davis CL, Wahid R, Toapanta FR, Simon JK, Sztein MB. A clinically parameterized mathematical model of Shigella immunity to inform vaccine design. PLoS One. 2018;13:e0189571 The authors build a mechanistic differential equation models of the gut immune response to Shigella in humans. Using Latin hypercube sampling and Monte Carlo simulations for parameter estimation they fit their model to human immune data from two Shigella vaccine trails and a rechallenge study. From their work, they concluded that antibody-based vaccines that target lipopolysaccharide or proteins on Shigella’s outer membrane are unlikely to sufficiently protect against severe disease, deploying sensitivity analysis to identify other possible targets for further study.

PubMed  PubMed Central  Google Scholar 

Khoury DS, Aogo R, Randriafanomezantsoa-Radohery G, McCaw JM, Simpson JA, McCarthy JS, et al. Within-host modeling of blood-stage malaria. Immunol Rev. 2018;285:168–93.

CAS  PubMed  Google Scholar 

Dodd PJ, Sismanidis C, Seddon JA. Global burden of drug-resistant tuberculosis in children: a mathematical modelling study. Lancet Infect Dis. 2016;16:1193–201.

PubMed  Google Scholar 

Kucharski AJ, Russell TW, Diamond C, Liu Y, Edmunds J, Funk S, et al. Early dynamics of transmission and control of COVID-19: a mathematical modelling study. Lancet Infect Dis. 2020;20:553–8.

CAS  PubMed  PubMed Central  Google Scholar 

Knight GM, Davies NG, Colijn C, et al. Mathematical modelling for antibiotic resistance control policy: do we know enough? BMC Infect Dis. 2019;19:1–9.

Google Scholar 

Garira W. A primer on multiscale modelling of infectious disease systems. Infect Dis Model. 2018;3:176–91.

PubMed  PubMed Central  Google Scholar 

Ming RX, Liu JM, William WK, Wan X. Stochastic modelling of infectious diseases for heterogeneous populations. Infect Dis Poverty. 2016;5:1–11.

Google Scholar 

Chang SL, Piraveenan M, Pattison P, Prokopenko M. Game theoretic modelling of infectious disease dynamics and intervention methods: a review. J Biol Dyn. 2020;14:57–89.

PubMed  Google Scholar 

El Jarroudi M, Karjoun H, Kouadio L, El Jarroudi M. Mathematical modelling of non-local spore dispersion of wind-borne pathogens causing fungal diseases. Appl Math Comput. 2020;376:125107.

Google Scholar 

Agrebi S, Larbi A. Use of artificial intelligence in infectious diseases. Artif Intell Precis Heal. 2020:415–38.

Roddam AW (2001) Mathematical epidemiology of infectious diseases: model building, analysis and interpretation: O Diekmann and JAP Heesterbeek, 2000, Chichester: John Wiley pp. 303,£39.95. ISBN 0-471-49241-8.

Kermack WO, McKendrick AG. A contribution to the mathematical theory of epidemics. Proc R Soc london Ser A, Contain Pap a Math Phys character. 1927;115:700–21.

Google Scholar 

Andraud M, Hens N, Marais C, Beutels P. Dynamic epidemiological models for dengue transmission: a systematic review of structural approaches. PLoS One. 2012;7:e49085. https://doi.org/10.1371/journal.pone.0049085.

CAS  Article  PubMed  PubMed Central  Google Scholar 

Mukhtar AYA, Munyakazi JB, Ouifki R, Clark AE. Modelling the effect of bednet coverage on malaria transmission in South Sudan. PLoS One. 2018;13:1–22.

Google Scholar 

Li R, Pei S, Chen B, Song Y, Zhang T, Yang W, Shaman J (2020) Substantial undocumented infection facilitates the rapid dissemination of novel coronavirus (SARS-CoV-2). Science (80- ) 368:489–493.

Eftimie R, Gillard JJ, Cantrell DA. Mathematical models for immunology: current state of the art and future research directions. Bull Math Biol. 2016;78:2091–134.

CAS  PubMed  PubMed Central  Google Scholar 

Germain RN, Meier-Schellersheim M, Nita-Lazar A, Fraser IDC. Systems biology in immunology: a computational modeling perspective. Annu Rev Immunol. 2011;29:527–85.

CAS  PubMed  PubMed Central  Google Scholar 

Perelson AS, Ribeiro RM. Introduction to modeling viral infections and immunity. Immunol Rev. 2018;285:5–8.

CAS  PubMed  PubMed Central  Google Scholar 

Opatowski L, Guillemot D, Boëlle P-Y, Temime L. Contribution of mathematical modeling to the fight against bacterial antibiotic resistance. Curr Opin Infect Dis. 2011;24:279–87.

PubMed  Google Scholar 

He Y, Rappuoli R, De Groot AS, Chen RT. Emerging vaccine informatics. J Biomed Biotechnol. 2010;2010.

Pappalardo F, Flower D, Russo G, Pennisi M, Motta S. Computational modelling approaches to vaccinology. Pharmacol Res. 2015;92:40–5.

PubMed  Google Scholar 

Levine MM, Kotloff KL, Barry EM, Pasetti MF, Sztein MB. Clinical trials of Shigella vaccines: two steps forward and one step back on a long, hard road. Nat Rev Microbiol. 2007;5:540–53.

CAS  PubMed  PubMed Central  Google Scholar 

Plotkin SA, Gilbert PB. Nomenclature for immune correlates of protection after vaccination. Clin Infect Dis. 2012;54:1615–7.

PubMed  PubMed Central  Google Scholar 

Arevalillo JM, Sztein MB, Kotloff KL, Levine MM, Simon JK. Identification of immune correlates of protection in Shigella infection by application of machine learning. J Biomed Inform. 2017;74:1–9.

PubMed  PubMed Central  Google Scholar 

Davis CL, Wahid R, Toapanta FR, Simon JK, Sztein MB, Levy D. Applying mathematical tools to accelerate vaccine development: modeling Shigella immune dynamics. PLoS One. 2013;8:e59465.

CAS  PubMed  PubMed Central  Google Scholar 

Houben RMGJ, Dodd PJ. The global burden of latent tuberculosis infection: a re-estimation using mathematical modelling. PLoS Med. 2016;13:1–13.

Google Scholar 

Uplekar M, Weil D, Lonnroth K, Jaramillo E, Lienhardt C, Dias HM, et al. WHO’s new end TB strategy. Lancet. 2015;385:1799–801.

PubMed  Google Scholar 

• Ekins S, Perryman AL, Clark AM, Reynolds RC, Freundlich JS. Machine learning model analysis and data visualization with small molecules tested in a mouse model of Mycobacterium tuberculosis infection (2014–2015). J Chem Inf Model. 2016;56:1332–43 The authors identify candidate compounds to pursue in mouse in vivo efficacy models for the treatment or Mycobaterium tuberculosis. They do this through machine learning and Bayesian models of in vivo Mycobaterium tuberculosis data generated by different laboratories using various mouse models. They show, for the first time, that consensus models can be used to predict in vivo activity of different treatment compounds and develop a new clustering method for data visualisation.

CAS  PubMed  PubMed Central  Google Scholar 

Ekins S, Pottorf R, Reynolds RC, Williams AJ, Clark AM, Freundlich JS. Looking back to the future: predicting in vivo efficacy of small molecules versus Mycobacterium tuberculosis. J Chem Inf Model. 2014;54:1070–82.

CAS  PubMed  PubMed Central  Google Scholar 

Salvatore PP, Becerra MC, zur Wiesch P, Hinkley T, Kaur D, Sloutsky A, et al. Fitness costs of drug resistance mutations in multidrug-resistant Mycobacterium tuberculosis: a household-based case-control study. J Infect Dis. 2016;213:149–55.

PubMed  Google Scholar 

Davies J, Davies D. Origins and evolution of antibiotic resistance. Microbiol Mol Biol Rev. 2010;74:417–33.

CAS  PubMed  PubMed Central  Google Scholar 

Craig M, Jenner AL, Namgung B, Lee LP, Goldman A (2020) Engineering in medicine to address the challenge of cancer drug resistance: from micro- and nanotechnologies to computational and mathematical modeling. Chem. Rev. Under review.

Google Scholar 

Van Bunnik BAD, Woolhouse MEJ. Modelling the impact of curtailing antibiotic usage in food animals on antibiotic resistance in humans. R Soc Open Sci. 2017;4:161067.

PubMed  PubMed Central  Google Scholar 

Knight GM, Costelloe C, Deeny SR, Moore LSP, Hopkins S, Johnson AP, et al. Quantifying where human acquisition of antibiotic resistance occurs: a mathematical modelling study. BMC Med. 2018;16:137.

PubMed  PubMed Central  Google Scholar 

Tandogdu Z, Koves B, Cai T, Cek M, Tenke P, Naber K, et al. Condition-specific surveillance in health care-associated urinary tract infections as a strategy to improve empirical antibiotic treatment: an epidemiological modelling study. World J Urol. 2020;38:27–34.

CAS  PubMed  Google Scholar 

Wang Y, Yang YJ, Chen YN, Zhao HY, Zhang S. Computer-aided design, structural dynamics analysis, and in vitro susceptibility test of antibacterial peptides incorporating unnatural amino acids against microbial infections. Comput Methods Prog Biomed. 2016;134:215–23.

Google Scholar 

Georgiadou A, Lee HJ, Walther M, van Beek AE, Fitriani F, Wouters D, et al. Modelling pathogen load dynamics to elucidate mechanistic determinants of host--Plasmodium falciparum interactions. Nat Microbiol. 2019;4:1592–602.

CAS  PubMed  PubMed Central  Google Scholar 

Khoury DS, Cromer D, Akter J, Sebina I, Elliott T, Thomas BS, et al. Host-mediated impairment of parasite maturation during blood-stage Plasmodium infection. Proc Natl Acad Sci. 2017;114:7701–6.

CAS  PubMed  Google Scholar 

Wale N, Jones MJ, Sim DG, Read AF, King AA. The contribution of host cell-directed vs. parasite-directed immunity to the disease and dynamics of malaria infections. Proc Natl Acad Sci. 2019;116:22386–92.

CAS  PubMed  Google Scholar 

•• Hogan AB, Winskill P, Verity R, Griffin JT, Ghani AC. Modelling population-level impact to inform target product profiles for childhood malaria vaccines. BMC Med. 2018;16:1–11 The authors simulated the changing anti-circumsporozoite antibody titre following vaccination for Plasmodium falciparum malaria and related the antibody titre to vaccine efficacy. The model they developed pairs an individual-based model of human transmission process with a stochastic compartment for the mosquito biology. Their study predicted the most important characteristics of malaria vaccines and showed how vaccine properties translate to public health outcomes.

Google Scholar 

Gaur AH, McCarthy JS, Panetta JC, et al. Safety, tolerability, pharmacokinetics, and antimalarial efficacy of a novel Plasmodium falciparum ATP4 inhibitor SJ733: a first-in-human and induced blood-stage malaria phase 1a/b trial. Lancet Infect Dis. 2020;20:964–75.

CAS  PubMed  Google Scholar 

Winskill P, Slater HC, Griffin JT, Ghani AC, Walker PGT. The US President’s malaria initiative, Plasmodium falciparum transmission and mortality: a modelling study. PLoS Med. 2017;14:e1002448.

PubMed  PubMed Central  Google Scholar 

White MT, Griffin JT, Churcher TS, Ferguson NM, Basáñez M-G, Ghani AC. Modelling the impact of vector control interventions on Anopheles gambiae population dynamics. Parasit Vectors. 2011;4:153.

PubMed  PubMed Central  Google Scholar 

Khoury DS, Cromer D, Elliott T, Soon MSF, Thomas BS, James KR, et al. Characterising the effect of antimalarial drugs on the maturation and clearance of murine blood-stage Plasmodium parasites in vivo. Int J Parasitol. 2017;47:913–22.

CAS  PubMed  Google Scholar 

Aogo RA, Khoury DS, Cromer D, Elliott T, Akter J, Fogg LG, et al. Quantification of host-mediated parasite clearance during blood-stage Plasmodium infection and anti-malarial drug treatment in mice. Int J Parasitol. 2018;48:903–13.

PubMed