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  "Title": "Pandemic Prediction Model in an SIRS Framework",
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  "Authors@R": "c(person(given = \"Razvan\", \nfamily = \"Romanescu\",\nrole = c(\"aut\", \"cre\"),\nemail = \"razvan_romanescu@hotmail.com\"),\nperson(given = \"Songdi\",\nfamily = \"Hu\",\nrole = \"aut\"),\nperson(given = \"Md Ashiqul\",\nfamily = \"Haque\",\nrole = \"aut\"),\nperson(given = \"Olivier\",\nfamily = \"Tremblay-Savard\",\nrole = \"aut\"))",
  "Description": "A suite of methods to fit and predict case count data\nusing a compartmental SIRS (Susceptible – Infectious –\nRecovered – Susceptible) model, based on an assumed\nspecification of the effective reproduction number. The\nsignificance of this approach is that it relates epidemic\nprogression to the average number of contacts of infected\nindividuals, which decays as a function of the total\nsusceptible fraction remaining in the population. The main\nfunctions are pred.curve(), which computes the epidemic curve\nfor a set of parameters, and estimate.mle(), which finds the\nbest fitting curve to observed data. The easiest way to pass\narguments to the functions is via a config file, which contains\ninput settings required for prediction, and the package offers\ntwo methods, navigate_to_config() which points the user to the\nconfiguration file, and re_predict() for starting the\nfit-predict process. The main model was published in Razvan G.\nRomanescu et al. <doi:10.1016/j.epidem.2023.100708>.",
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  "Author": "Razvan Romanescu [aut, cre], Songdi Hu [aut], Md Ashiqul Haque\n[aut], Olivier Tremblay-Savard [aut]",
  "Maintainer": "Razvan Romanescu <razvan_romanescu@hotmail.com>",
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