Inserting Models

WARNING
The methods presented in this documentation generate real objects in database. To test Mosqlimate API request methods without inserting data, please refer to API Demo

Input parameters

Parameter name	Type	Description
name	str	Model name
description	str or None	Model description
repository	str	Github repository URL
implementation_language	str	Implementation language
disease	str (iexact)	Model disease. Options: Dengue, Zika and Chikungunya
temporal	bool	Is the model temporal?
spatial	bool	Is the model spatial?
categorical	bool	Is the model categorical?
adm_level	int (0, 1, 2 or 3)	Administrative level, options: 0, 1, 2, 3 (National, State, Municipality, Sub Municipality)
time_resolution	str (iexact)	Options are: day, week, month or year
sprint	bool	Model for Sprint 2024/25

Implementation Language

Currently, Mosqlimate supports the following implementation languages:

Python	C	C#	C++
CoffeeScript	.NET	Erlang	Go
Haskell	JavaScript	Java	Kotlin
Lua	R	Ruby	Rust
Zig

If you don't see your Implementation Language in the list, please contact the moderation.

X-UID-Key

POST requests require User API Token to be called.

Usage examples

The mosqlient package also accepts a pandas DataFrame with the required keys as an alternative to a JSON in the prediction parameter. For more details, refer to the documentation here.

Python3RCURL

from mosqlient import upload_model 

upload_model(
    name = "My Nowcasting Model",
    description = "My Model description",
    repository = "https://github.com/Mosqlimate-project/Data-platform",
    implementation_language = "Python",
    disease = "dengue",
    temporal = False,
    spatial = True,
    categorical = False,
    adm_level = 0, # National
    time_resolution = "week",
    sprint = False
)

library(httr)
library(jsonlite)

# Warning: this method generates real object in the database if called with
# the correct UID Key
post_model <- function(
    name,
    description,
    repository,
    implementation_language,
    disease,
    spatial,
    temporal,
    categorical,
    adm_level,
    time_resolution,
    sprint
) {
url <- "https://api.mosqlimate.org/api/registry/models/"

headers <- add_headers(
    `X-UID-Key` = X-UID-Key)

model <- list(
    name = name,
    description = description,
    repository = repository,
    implementation_language = implementation_language,
    disease = disease,
    spatial = spatial,
    temporal = temporal,
    categorical = categorical,
    adm_level = adm_level,
    time_resolution = time_resolution,
    sprint = sprint
    )

response <- POST(url, headers, body = model,  encode = "json")
return(content(response, "text"))
}

# Example
post_model(
  name = "My Nowcasting Model",
  description = "My Model description",
  repository = "https://github.com/Mosqlimate-project/Data-platform",
  implementation_language = "R",
  disease = "dengue",
  spatial = TRUE,
  temporal = FALSE,
  categorical = TRUE,
  adm_level = 0,
  time_resolution = "week",
  sprint = FALSE
)

curl -X 'POST' \
    'https://api.mosqlimate.org/api/registry/models/' \
    -H 'accept: application/json' \
    -H 'X-UID-Key: See X-UID-Key documentation' \
    -H 'Content-Type: application/json' \
    -d '{
    "name": "My Nowcasting Model",
    "description": "My Model description",
    "repository": "https://github.com/Mosqlimate-project/Data-platform",
    "implementation_language": "Python",
    "disease": "dengue",
    "spatial": true,
    "temporal": true,
    "categorical": true,
    "adm_level": 0,
    "time_resolution": "week",
    "sprint": false
}'