ArboMAP 4.5 Released 2 June 2023

ArboMAP 4.5 Released 2 June 2023

Minor update: Fixed a problem with formatting of the parameter summary table that was causing an error when the appendix was generated.

ArboMAP 4.4 Released 27 October 2022

ArboMAP 4.4 Released 27 October 2022

Minor update: Corrected the count of counties with relative risk higher than average risk displayed in the report.

Arbovirus Modeling and Prediction to Forecast Mosquito-Borne Disease Outbreaks (ArboMAP) is a set of software to be used in the RStudio environment to model and predict vector-borne diseases, especially arboviruses transmitted by mosquitoes. In this demo project, ArboMAP is being used for forecasting West Nile virus.

Important Note: The human and mosquito data that come packaged with ArboMAP are synthetic data, created by first fitting the model on West Nile virus in South Dakota, and then generating human cases and mosquito pools according to that model. Hence, while they are consistent with the overall trends of actual data, they are not the actual data, and must not be used as a basis for scientific inference. Rather, they are provided so that the user can see an example of the code working well with realistic data.

Start with ArboMAP_user_guide.pdf found attached to the Github release, or in the documentation folder.

ArboMAP Version 4.3 Released 03 August 2022

ArboMAP 4.3 Released 03 August 2022

• Revised moquito infection rate imputation method. When modeling years where human cases are known and mosquito pool data are not available, ArboMAP will impute values for modeling. Imputation now better preserves the relationship between total human cases and MIR statistic. Previously, having many years of unknown mosquito data could end up with the model overly reliant on environmental data instead.

• Changed default mosquito model to be MIGR if user input was not able to be matched.

• Corrected mosquito pool 2-week date range so that the LAST day of the second epiweek is displayed, not the first day of the epiweek.

• Fixed a bug in the report when creating a pdf and the user selected a file for an input parameter using the rmarkdown GUI.

• Fixed a bug in the appendix that was preventing report generation when a new user-specified formula was used in the models.txt file.

• Updated documentation and quick guides.

Arbovirus Modeling and Prediction to Forecast Mosquito-Borne Disease Outbreaks (ArboMAP) is a set of software to be used in the RStudio environment to model and predict vector-borne diseases, especially arboviruses transmitted by mosquitoes. In this demo project, ArboMAP is being used for forecasting West Nile virus.

Important Note: The human and mosquito data that come packaged with ArboMAP are synthetic data, created by first fitting the model on West Nile virus in South Dakota, and then generating human cases and mosquito pools according to that model. Hence, while they are consistent with the overall trends of actual data, they are not the actual data, and must not be used as a basis for scientific inference. Rather, they are provided so that the user can see an example of the code working well with realistic data.

Start with ArboMAP_user_guide.pdf found attached to the Github release, or in the documentation folder.

ArboMAP Version 4.2 Released 23 June 2022

Minor updates:

• Fixed an error that sometimes occurred in mosquito positivity rate graph when there was only a small amount of pools available in the forecast year.
• Added pool positive percentage to the mosquito pool summary table.
• Minor edits and clarifications to report narrative and figures.
• Removed unused tex package; removed internal objects during processing to reduce memory usage.
• Edited documentation reflecting that html is the default report format, with additional setup for pdf capability.
• Other minor fixes.

Arbovirus Modeling and Prediction to Forecast Mosquito-Borne Disease Outbreaks (ArboMAP) is a set of software to be used in the RStudio environment to model and predict vector-borne diseases, especially arboviruses transmitted by mosquitoes. In this demo project, ArboMAP is being used for forecasting West Nile virus.

Important Note: The human and mosquito data that come packaged with ArboMAP are synthetic data, created by first fitting the model on West Nile virus in South Dakota, and then generating human cases and mosquito pools according to that model. Hence, while they are consistent with the overall trends of actual data, they are not the actual data, and must not be used as a basis for scientific inference. Rather, they are provided so that the user can see an example of the code working well with realistic data.

Start with ArboMAP_user_guide.pdf found attached to the Github release, or in the documentation folder.

ArboMAP Version 4.1 Released 1 June 2022

Minor updates:

• Corrected report narrative and graphs for mosquito data summary when there is no mosquito data for the current forecast year yet.
• Corrected mosquito year start to start of synthetic data (2004).
• Minor formatting changes to stratified relative risk mosquito plots.

Arbovirus Modeling and Prediction to Forecast Mosquito-Borne Disease Outbreaks (ArboMAP) is a set of software to be used in the RStudio environment to model and predict vector-borne diseases, especially arboviruses transmitted by mosquitoes. In this demo project, ArboMAP is being used for forecasting West Nile virus.

Important Note: The human and mosquito data that come packaged with ArboMAP are synthetic data, created by first fitting the model on West Nile virus in South Dakota, and then generating human cases and mosquito pools according to that model. Hence, while they are consistent with the overall trends of actual data, they are not the actual data, and must not be used as a basis for scientific inference. Rather, they are provided so that the user can see an example of the code working well with realistic data.

Start with ArboMAP_user_guide.pdf found attached to the Github release, or in the documentation folder.

ArboMAP Version 4.0 Released 13 May 2022

Version 4.0 is a major update:

• Massive rewrite of the report output and format, including figures and narrative, based on feedback from public health partners.
• Significant changes to user interfaces and input parameters.
• Overhaul of the internal code, switching to mostly tidyverse data processing and presentation R packages.
• Fixed environmental data processing to always use the latest updated value for any particular day when there are overlaps between data files.
• Updated GEE code to code_GEE/arbomap_gridmet_gee_v2_2.js.
• Minor bug fixes and addition of copious developer and code comments for future developers.

Arbovirus Modeling and Prediction to Forecast Mosquito-Borne Disease Outbreaks (ArboMAP) is a set of software to be used in the RStudio environment to model and predict vector-borne diseases, especially arboviruses transmitted by mosquitoes. In this demo project, ArboMAP is being used for forecasting West Nile virus.

Important Note: The human and mosquito data that come packaged with ArboMAP are synthetic data, created by first fitting the model on West Nile virus in South Dakota, and then generating human cases and mosquito pools according to that model. Hence, while they are consistent with the overall trends of actual data, they are not the actual data, and must not be used as a basis for scientific inference. Rather, they are provided so that the user can see an example of the code working well with realistic data.

Start with ArboMAP_user_guide.pdf found attached to the Github release, or in the documentation folder.

ArboMAP Version 3.2 Released 29 June 2021

Version 3.2 is a minor update.
It includes a revised User's Guide, with multiple updated instructions, including how to use TinyTex instead of MiKTeX for pdf report generation.

Previous version 3.1 notes:
The main script to generate ArboMAP reports, ArboMAP_Main_Code.Rmd, now can be adjusted using parameters via utilizing RStudio's "Knit with Parameters" feature. This allows for making modifications without changing the original settings.

ArboMAP Version 3.1 Released 16 June 2021

This release streamlines the ability to run the report with different settings.

• The main script to generate ArboMAP reports, ArboMAP_Main_Code.Rmd, now can be adjusted using parameters via utilizing RStudio's "Knit with Parameters" feature. This allows for making modifications without changing the original settings.
• Minor bug fixes and re-organization of the project.

ArboMAP 3.0

We have reconciled analyses used on four states (SD, OK, LA, and MI), each of which required slightly different approaches to the data. The synthetic data packaged with ArboMAP concern SD, but files from elsewhere are now compatible with this version.

• There are now multiple methods of summarizing the mosquito infection data, apart from the original stratified mosquito infection growth rate. This now includes the estimated intercept (MII, stratified or not) and the area under the estimated mosquito infection curve (AUC). There is also the simple ratio of positive pools to total pools tested (simpleratio).
• Flat mosquito data (i.e. mosquito data presented solely as positive pools and total pools tested per year) can be resampled by referencing timed mosquito data from another source.
• Filenames are now given so that the download should work directly with Mac machines.

ArboMAP 2.3

Data are now associated with a "believable" parameter, which requires the user to tell ArboMAP which data can be trusted when. This is less clever than trying to infer the limits of credibility from the files, but it is also more robust against, for example, data missing in the middle of a time series rather than on its edges.

We no longer attempt to figure out when the user wants to do predictions - the user chooses the list of years for which estimates should be produced.

Shapefiles are now downloaded with the tigris package during the run, rather than requiring the user to produce and store them before running the program.

Estimates of random effects in the mosquito model are now more robust. Occasionally, numerics would be forcibly converted to factors whenever the numerics were all integers, for example, and this would cause problems in the estimates. We now allow missing mosquito data in the middle of the time series.

We have now adjusted the model to include indicators whenever a year has believable human data that is to be modeled, but there is no associated mosquito data. In this case, the missing mosquito data are assumed to be average and the intercept is simply fit with a constant for that year.

There have been changes in language throughout to better explain the workings of the program.