2.1 Add Model-Based Predictions

This code adds in model-based predictions for the ‘type’ of policy an observation is provided in the who_predicts.csv file. This data was provided by the Coronanet Data Science Team. The models used to create those predictions were trained on the Coronanet data, those predictions might therefore not be accurate and are to be treated as suggestive. ‘type_alt’ refers to the more confident guess by the models and ‘type_alt_alt’ contains the second best predictions from these predictive models.

amp_coronanet_map = amp_coronanet_map %>%  
  left_join(amp_predicts %>% 
  dplyr::select(unique_id = `Unique ID` , 
                type_alt = predict_1,
                type_alt_2 = predict_2))  

2.2 Countries

The following code adjust for the different ways each tracker documents policies originating from certain regions of the world. In particular:

• COVID AMP categorizes England, Northern Ireland, Scotland and Wales as separate countries. The CoronaNet taxonomy considers these regions as provinces within the United Kingdom and the following code adjusts this data accordingly.

• COVID AMP categorizes tribal nations in the United States as separate countries. The CoronaNet taxonomy considers these regions as local governmental levels within the United States and the following code adjusts this data accordingly.

amp_coronanet_map = amp_coronanet_map %>% 
  mutate(
      province = case_when(
      is.na(country) & country == "England (GB-ENG)" ~ "England",
      is.na(country) & country ==  "Northern Ireland (GB-NIR)" ~ "Northern Ireland",
      is.na(country) & country ==  "Scotland (GB-SCT)" ~ "Scotland", 
      is.na(country) & country == "Wales (GB-WLS)" ~ "Wales" ,
      grepl("Tribe|Sac and Fox Nation|Yavapai Nation|Blackfeet Nation|Navajo|Crow Nation|Community|Colony|Reservation|Potawatomi Nation|Tohono O'odham|Luik", province) ~ as.character(NA),
      TRUE ~ province),
    init_other = case_when(
grepl("Tribe|Sac and Fox Nation|Yavapai Nation|Blackfeet Nation|Navajo|Crow Nation|Community|Colony|Reservation|Potawatomi Nation|Tohono O'odham|Luik", country) ~ country,
      TRUE~init_other),
    country = case_when(
      is.na(country) & country == "England (GB-ENG)" ~ "United Kingdom" ,
      is.na(country) & country ==  "Northern Ireland (GB-NIR)" ~ "United Kingdom",
      is.na(country) & country ==  "Scotland (GB-SCT)" ~ "United Kingdom",
      is.na(country) & country == "Wales (GB-WLS)"  ~ "United Kingdom",
      grepl("Tribe|Sac and Fox Nation|Yavapai Nation|Blackfeet Nation|Navajo|Crow Nation|Community|Colony|Reservation|Potawatomi Nation|Tohono O'odham|Luik", country) ~ "United States of America",
      TRUE~ country),
    ISO_A3 = 
      case_when(
        ISO_A3 %in% c('GB-SCT', "GB-ENG", 'GB-WLS', "GB-NIR")~ "GBR",
        grepl("Tribe|Sac and Fox Nation|Yavapai Nation|Blackfeet Nation|Navajo|Crow Nation|Community|Colony|Reservation|Potawatomi Nation|Tohono O'odham|Luik", init_other) ~ "USA",
        TRUE ~ ISO_A3
      ),
    
  )

2.3 Cities and Counties

The COVID AMP variable Authorizing_local_area does not make distinctions between cities and counties whereas the CoronaNet taxonomy does. To the extent possible, the following code separates out cities and counties through the use of regular expressions into the city and init_other variables in the CoronaNet taxonomy accordingly.

init <- covidamp %>%
  mutate(
    init_other  = case_when(
         grepl("County|county", Authorizing_local_area) ~ Authorizing_local_area,
         TRUE ~ as.character(NA)
    ),
    city = case_when(
      grepl("Luik", Authorizing_local_area) ~ Authorizing_local_area,
      grepl("city|City", Authorizing_local_area) ~ Authorizing_local_area,
      is.na(init_other) ~  Authorizing_local_area,
      TRUE ~ as.character(NA)
    )
  ) %>% select(
    unique_id, init_other, city
  )
amp_coronanet_map = rows_patch(amp_coronanet_map, init, "unique_id")

2.4 End dates

COVID AMP captures information on end dates in two separate variables, the anticipated end date and actual end date. The closest match for these variables in the CoronaNet taxonomy is its date_end (Note, additionally the CoronaNet taxonomy also captures qualitative information about end dates in its date_end_spec variable but this is not a close conceptual match ot the COVID AMP variables). In order to retain as much information as possible from COVID AMP about the end dates, the following information is mapped into the CoronaNet date_end variable.

• If te anticipated end date is the same as the actual end date and neither of them is NA, the actual end date is used.
• If the anticipated end date is different than the actual end date and neither of them is NA, both of the end dates are used.
• If there is only an actual end date captured and no anticipated end date, the actual end date is used.
• If there is only an anticipated end date captured and no actual end date, the anticipated end date is used.
• If there is no information recorded in either anticipated end date and actual end date, then no end date is recorded.

end_date =
  covidamp %>%
  mutate(
    date_end =
      case_when(
        !is.na(Actual_end_date) & !is.na(Anticipated_end_date) & Actual_end_date == Anticipated_end_date ~  Actual_end_date,
        !is.na(Actual_end_date) & !is.na(Anticipated_end_date) & Actual_end_date != Anticipated_end_date ~  paste0("Actual end date:",  Actual_end_date, ' ; anticipated end date: ', Anticipated_end_date),
        !is.na(Actual_end_date) & is.na(Anticipated_end_date)  ~   Actual_end_date,
        is.na(Actual_end_date) & !is.na(Anticipated_end_date)  ~   Anticipated_end_date,
        !is.na(Actual_end_date) & !is.na(Anticipated_end_date) ~ as.character(NA)
      )
  ) %>% select(unique_id, date_end)
amp_coronanet_map = rows_patch(amp_coronanet_map,end_date, "unique_id")

2.5 Policy Level

COVID AMP captures information on the level of government that authorizes a given policy action in its Authorizing_level_of_government variable, which is a direct conceptual match for CoronaNet’s init_country_level variable. The values for this field however need to be adjusted slightly to match the CoronaNet taxonomy:

• Policies originating from England, Northern Ireland, Scotland and Wales are changed from policies that originate at the Country level in the COVID AMP taxonomy to the Provincial level in the CoronaNet taxonomy, since CoronaNet considers these regions as provinces of Great Britain rather than as separate countries as COVID AMP does.
• Policies coded as taking the value of Country in the COVID AMP taxonomy are renamed as National to match the CoronaNet taxonomy
• Policies coded as taking the value of State/Province in the COVID AMP taxonomy are renamed as Provincial to match the CoronaNet taxonomy
• Policies coded as taking the value of Tribal nation in the COVID AMP taxonomy are kept as is although there is no corresponding category in the CoronaNet taxonomy. These policies will be processed later during the harmonisation process.
• Policies coded as taking the value of Local in the COVID AMP taxonomy are alternatively coded as taking the value of Other (e.g., county) or Municipal depending on whether the kewords of country or city are present in the localities name.

In addition, the CoronaNet taxonommy also documents whether the government which initiates a policy is the same as the geographic target of the policy in its target_init_same variable. We extract this information from the COVID AMP data as follows:

• target_init_same (i.e. whether the geographical target of the policy matches the geographical location the authorizing governemnt body sits in) takes the value of Yes if the Authorizing_country_name is the same as the Affected_country_name and No otherwise.

pol_level = covidamp %>%
  select(unique_id, Authorizing_level_of_government, Authorizing_country_ISO,Authorizing_country_name, Affected_country_ISO, Authorizing_local_area) %>%
  mutate(init_country_level = case_when(
  Authorizing_country_name  %in% c( "England (GB-ENG)", 
                         "Northern Ireland (GB-NIR)",
                         "Scotland (GB-SCT)",
                         "Wales (GB-WLS)") ~ "Provincial",
    # grepl("Tribe|Sac and Fox Nation|Yavapai Nation|Blackfeet Nation|Navajo|Crow Nation|Community|Colony|Reservation|Potawatomi Nation|Tohono O'odham|Luik", Authorizing_country_name)~ "Other (e.g., county)",
    Authorizing_level_of_government == "Country" ~ "National",
    Authorizing_level_of_government == "State / Province" ~ "Provincial",
    Authorizing_level_of_government == "Tribal nation" ~ "Tribal nation",
    Authorizing_level_of_government == "Local"  &    grepl("County|county", Authorizing_local_area) ~ "Other (e.g., county)",
    Authorizing_level_of_government == "Local"  &    grepl("city|City", Authorizing_local_area) ~ "Municipal",
       Authorizing_level_of_government == "Local"  &    !grepl("city|City|County|county", Authorizing_local_area) ~ "Other (e.g., county)",
  )) %>%
  mutate(target_init_same = case_when(
    Authorizing_country_name == Affected_country_name ~ "Yes",
    TRUE ~ "No"
  )) %>%
  select(unique_id, init_country_level)
amp_coronanet_map = rows_patch(amp_coronanet_map, pol_level, "unique_id")

2.6 Target Regions

The CoronaNet taxonommy also documents what level of government a policy targets in its target_geog_level variable. We extract this information from the COVID AMP data as follows:

• If a policy does not recorded a state or province or local area that it specifically targets, target_geog_level takes on the value of One or more countries, but not all countries
• If a policy records state or province that it specifically targets but no local area that it specifically targets, target_geog_level takes on the value of One or more provinces within a country
• If a policy records a local area that it specifically targets, target_geog_level takes on the value of One or more cities within a country

target_reg = covidamp %>%
  select(unique_id, Affected_country_name, Affected_state_province, Affected_local_area) %>%
  mutate(target_geog_level = case_when(
    is.na(Affected_state_province) & is.na(Affected_local_area) ~ "One or more countries, but not all countries",
    is.na(Affected_local_area) ~ "One or more provinces within a country",
    !is.na(Affected_local_area) ~ "One or more cities within a country"
  )) %>%
  select(-c(2,3,4))
  
amp_coronanet_map = rows_patch(amp_coronanet_map, target_reg, "unique_id")

2.7 Policy update level

• The COVID AMP Policy_relaxing_or_restricting, which records whether a policy is relaxing or strenthening over tie, is nearly direct conceptual match for CoronaNet’s update_level. Both CoronaNet and COVID AMP refer to policies that ease over time as Relaxing. COVID AMP refers to policies that become more restrictive over time as Restricting while CoronaNet refers to such policies as Strenthening.

• However, COVID AMP’s value 9of Other for the variable cannot be directly mapped to the CoronaNet taxonomy; such mappings with be done downstream during manual harmonisation. For the purposes of this mapping, these are recoded as NA.

amp_coronanet_map$update_level = covidamp$Policy_relaxing_or_restricting
amp_coronanet_map$update_level = na_if(amp_coronanet_map$update_level, "Other")

2.8 Target Who/ What

The COVID AMP taxonomy captures information as to the demographics of a policy in its Policy_subtargets, which is a direct conceptual map to the target_who_gen variable in the CoronaNet taxonomy. Not all values within this variable are directly mappable however. The following code maps as many values as are directly mappable as possible and leaves the rest for manual harmonisation to resolve downstream.

temp = covidamp %>% 
  select(unique_id, Policy_subtarget) %>%
  mutate(target_who_gen = case_when(
    grepl("Nursing homes/assisted living facility", Policy_subtarget) ~ "People in nursing homes/long term care facilities",
    grepl("Homeless shelters/homeless individuals", Policy_subtarget) ~ "Homeless population",
    grepl("Non essential workers (inclusive)", Policy_subtarget) ~ "Non-essential workers (please note their occupation in the text entry where applicable)",
    grepl("Essential workers (inclusive)",Policy_subtarget) ~ "Essential workers (please note their occuption in the text entry where applicable)",
    grepl("Migrant workers/Refugees/asylum seekers", Policy_subtarget) ~ "Migrant workers (please note their occupation in the text entry where applicable)",
  ),
  target_who_what = case_when(grepl("Travelers", Policy_subtarget) ~ "All Travelers (Citizen Travelers + Foreign Travelers)"
    )) %>%
  select(-c(2))
amp_coronanet_map = rows_patch(amp_coronanet_map, temp, "unique_id")

3.1 Social Distancing

The following code maps how COVID AMP captures social distancing policies to the CoronaNet taxonomy. While when there is no one to one matching of the Social distancing Policy_category to the CoronaNet taxonomy, it is possible to use the Policy_subcategory to create one to one matchings for the type variable in the CoronaNet taxonomy. In particular, the following code matches the following for policies that take on the value of Social distancing in the Policy_category variable::

• Observations that take on the value of Other forms of social distancing, Visitor restrictions, Distancing mandate, Face covering or takes on no value in Policy_subcategory variable can be mapped as Social Distancing for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Lockdown, Stay at home, Safer at home or Isolation in the Policy_subcategory variable can be mapped as Lockdown for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Alternative election measures, Public service closures or Election delays in the Policy_subcategory variable can be mapped as Restriction and Regulation of Government Services for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Private sector closures or Adaptation and mitigation measures in the Policy_subcategory variable can be mapped as Restriction and Regulation of Businesses for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Mass gathering restrictions, Event delays or cancellations, or Prison population reduction in the Policy_subcategory variable can be mapped as Restrictions of Mass Gatherings for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of School closures in the Policy_subcategory variable can be mapped as Closure and Regulation of Schools for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Quarantine in the Policy_subcategory variable can be mapped as Quarantine for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Curfews in the Policy_subcategory variable can be mapped as Curfew for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Health screening in the Policy_subcategory variable can be mapped as Health Monitoring for the type variable in the CoronaNet taxonomy.

social_distancing = covidamp %>%
  filter(Policy_category == "Social distancing") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type = case_when(
  
  Policy_subcategory %in% c(
    "Other forms of social distancing",
    "Visitor restrictions",
    "Distancing mandate" ,
    "Face covering"
    ) ~ "Social Distancing",
  
  Policy_subcategory %in% c( 
      "Lockdown",
      "Stay at home",
      "Safer at home",
      "Isolation") ~ "Lockdown",
  
  Policy_subcategory %in% c( 
    "Alternative election measures",
    "Public service closures",
    "Election delays"
    ) ~ "Restriction and Regulation of Government Services",
  
  Policy_subcategory %in% c(
    "Private sector closures",
    "Adaptation and mitigation measures"
    )~ "Restriction and Regulation of Businesses",
  
  
  Policy_subcategory %in% c( 
    "Mass gathering restrictions",
    "Event delays or cancellations",
    "Prison population reduction")~ "Restrictions of Mass Gatherings",
  
    
  Policy_subcategory == "School closures" ~ "Closure and Regulation of Schools",
  Policy_subcategory == "Quarantine" ~ "Quarantine",
  Policy_subcategory == "Curfews" ~ "Curfew",
  Policy_subcategory == "Health screening" ~ "Health Monitoring",
  
  
  is.na(Policy_subcategory) ~ "Social Distancing",
  
  
  
  TRUE ~ as.character(NA)
  ))%>%
  select(-c(2,3))

amp_coronanet_map = rows_patch(amp_coronanet_map, social_distancing, "unique_id")

3.2 Contact tracing/Testing

The following code maps how COVID AMP captures contact tracing and testing policies to the CoronaNet taxonomy. Though there is no one to one matching of the Contact tracing/Testing Policy_category to the CoronaNet taxonomy, it is possible to use the Policy_subcategory to create one to one matchings for the type variable in the CoronaNet taxonomy. In particular, the following code matches the following for policies that take on the value of Contact tracing/Testing in the Policy_category variable:

• Observations that take on the value of Testing or no vaolue Policy_subcategory variable can be mapped as Health Testing for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Contact tracing Policy_subcategory variable can be mapped as Health Monitoring for the type variable in the CoronaNet taxonomy. A more detailed mapping of to policy subtypes in the CoronaNet taxonomy would theoretically be possible but would not be possible to do easily. As such it was not done in this initial mapping but rather reserved for downstream manual data harmonization.

contacts = covidamp %>%
  filter(Policy_category == "Contact tracing/Testing") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type = case_when(
    Policy_subcategory == "Testing" ~ "Health Testing",
    Policy_subcategory == "Contact tracing" ~ "Health Monitoring",
    is.na(Policy_subcategory) ~ "Health Testing",
     TRUE ~ as.character(NA)
  ))%>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, contacts, "unique_id")

3.3 Support for public health and clinical capacity

The following code maps how COVID AMP captures support for public health and clinical capacity to the CoronaNet taxonomy.

• The COVID AMP taxonomy aims to capture such policies by coding the Policy_category as Support for public health and clinical capacity

• The CoronaNet taxonomy aims to capture such policies by coding the type variable as Health Resources

• Note while it may have been possible to addtionally map policy sub types within this larger policy type, such mappings would have been imprecise and we reserved them for downstream manual data harmoization.

support_health = covidamp %>%
  filter(Policy_category == "Support for public health and clinical capacity") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type =  "Health Resources")%>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, support_health, "unique_id")

3.4 Enabling and relief measures

The following code maps how COVID AMP captures relief measure policies to the CoronaNet taxonomy. Though there is no one to one matching of the Enabling and relief measures Policy_category to the CoronaNet taxonomy, it is possible to use the Policy_subcategory to create one to one matchings for the type variable in the CoronaNet taxonomy. In particular, the following code matches the following for policies that take on the value of Enabling and relief measures in the Policy_category variable:

• Observations that take on the value of Regulatory relief, Other relief measures, Relief funding , Eviction and foreclosure delays, Budget modifications, Modification of unemployment benefits, Leave entitlement adjustments, Other labor protections, Anti-price gouging measures, Utility payment delay, Utility payment, Stimulus payments, Mortgage payment support, Hazard pay", or takes on no value in Policy_subcategory variable can be mapped as Other Policy Not Listed Above (econ) for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Extension of public services, Tax delay, Remote Notarization in the Policy_subcategory variable can be mapped as Restriction and Regulation of Government Services for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Support for essential workers in the Policy_subcategory variable can be mapped as Health Resources for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Early prison release in the Policy_subcategory variable can be mapped as Restrictions of Mass Gatherings for the type variable in the CoronaNet taxonomy.

relief_measures = covidamp %>%
  filter(Policy_category == "Enabling and relief measures") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type = case_when(
    Policy_subcategory %in% c(
      "Regulatory relief",
      "Other relief measures",
      "Relief funding" ,
      "Eviction and foreclosure delays",
      "Budget modifications",
      "Modification of unemployment benefits" ,
      "Leave entitlement adjustments",
      "Other labor protections" ,
      "Anti-price gouging measures",
      "Utility payment delay",
      "Utility payment",
      "Stimulus payments",
      "Mortgage payment support",
      "Hazard pay")~ "Other Policy Not Listed Above (econ)",
    
    Policy_subcategory %in% c(
      "Extension of public services",
      "Tax delay",
      "Remote Notarization"
    ) ~ "Restriction and Regulation of Government Services",
  
    Policy_subcategory == "Support for essential workers" ~ "Health Resources",
    Policy_subcategory == "Early prison release" ~ "Restrictions of Mass Gatherings",

    is.na(Policy_subcategory) ~ "Other Policy Not Listed Above (econ)",
    
    TRUE ~ as.character(NA)
  ))%>%
  select(-c(2,3))

amp_coronanet_map = rows_patch(amp_coronanet_map, relief_measures, "unique_id")

3.5 Travel restrictions

The following code maps how COVID AMP captures travel restriction policies to the CoronaNet taxonomy. Though there is no one to one matching of the Travel restrictions Policy_category to the CoronaNet taxonomy, it is possible to use the Policy_subcategory to create one to one matchings for the type variable in the CoronaNet taxonomy. In particular, the following code matches the following for policies that take on the value of Travel restrictions in the Policy_category variable:

• Observations that take on the value of International travel restriction, Domestic travel restrictions (interstate) or take on no value in the Policy_subcategory variable can be mapped as External Border Restrictions for the type variable in the CoronaNet taxonomy.

• Observations that take on the value of Domestic travel restriction or Domestic travel restrictions (intrastate) in the Policy_subcategory variable can be mapped as Internal Border Restrictions for the type variable in the CoronaNet taxonomy.

travel_restrictions = covidamp %>%
  filter(Policy_category == "Travel restrictions") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type = case_when(
    Policy_subcategory %in% c(
      "International travel restriction",
       "Domestic travel restrictions (interstate)") ~ "External Border Restrictions",
    Policy_subcategory %in% c(
      "Domestic travel restriction",
      "Domestic travel restrictions (intrastate)")~ "Internal Border Restrictions",

    is.na(Policy_subcategory) ~ "External Border Restrictions",
  TRUE ~ as.character(NA)
  ))%>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, travel_restrictions, "unique_id")

3.6 Emergency declarations

The following code maps how COVID AMP captures declarations of emergency to the CoronaNet taxonomy.

• The COVID AMP taxonomy aims to capture such policies by coding the Policy_category as Emergency declarations

• The CoronaNet taxonomy aims to capture such policies by coding the type variable as Declaration of Emergency

emergency = covidamp %>%
  filter(Policy_category == "Emergency declarations") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type = "Declaration of Emergency"
  )%>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, emergency, "unique_id")

3.7 Military mobilization

The following code maps how COVID AMP captures military mobilization to the CoronaNet taxonomy.

• The COVID AMP taxonomy aims to capture such policies by coding the Policy_category as *Military mobilization**

• The CoronaNet taxonomy does not systematically capture policies about military mobilization. As such, these policies have been mapped such that thetype variable takes the value of Other Policy Not Listed Above

CoronaNet does not capture policies about military mobilization, therefore it has been mapped to ‘Other Policy Not Listed Above’.

military = covidamp %>%
  filter(Policy_category == "Military mobilization") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type = "Other Policy Not Listed Above"
  ) %>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, military, "unique_id")

3.8 Authorization and Enforcement

The following code maps how COVID AMP captures policies focused on giving various (non-) governmental bodies more authorization and how the enforcment of policies is handled to the CoronaNet taxonomy despite the fact that it cannot be directly done. Note that while CoronaNet captures how policies are enforced, but does not capture changes in the enforcement as stand-alone policies but rather in its compliance variable.

• On manual inspection of the policies coded with the value of Authorization and enforcement for the variable Policy_category and Enforcement for the variable Policy_subcategory, we found that they could be mapped as either New Task Force, Bureau or Administrative Configuration, Restriction and Regulation of Businesses, Restrictions of Mass Gatherings, Restriction and Regulation of Government Services, Social Distancing, Curfew, Quarantine,Lockdown, Health Testing, External Border Restrictions, Internal Border Restrictions or Other Policy Not Listed Above

• Based on keywords in the textual description of these policies, which we identified based on manual inspection of the data, we customized the code to sort these policies into different values in CoronaNet’s type variable, with Other Policy Not Listed Above being the default option when no other keyword was found. While this does not provide a perfect mapping of these policies, it is sufficient for a large proportion of the data; downstream manual harmonization will correct for any mis-mappings

authorization = covidamp %>%
  filter(Policy_category == "Authorization and enforcement") %>%
  select(unique_id, Policy_category, Policy_subcategory, Policy_description) %>%
  mutate(type = case_when(
    Policy_subcategory == "Authorization" ~ "New Task Force, Bureau or Administrative Configuration",
    
    Policy_subcategory == "Enforcement"  & grepl("fitness|bars|kiosk|Business|alcohol|workplace|employ|worker|food|store|business|sport|Bank|bank|Personal care servi|Floral|estaurant", Policy_description) ~ "Restriction and Regulation of Businesses",
    Policy_subcategory == "Enforcement"  & 
      grepl("density|together in a public|together in public|gathering", Policy_description) ~ "Restrictions of Mass Gatherings",
    Policy_subcategory == "Enforcement"  & 
      grepl("public seating|campground|license", Policy_description) ~ "Restriction and Regulation of Government Services" ,
    Policy_subcategory == "Enforcement"  & 
      grepl("education|school", Policy_description) ~ "Restriction and Regulation of Government Services" ,
    Policy_subcategory == "Enforcement"  & 
      grepl("physical distanc|face cover|nursing|6ft|mask|meter|social distancing", Policy_description) ~ "Social Distancing" ,
    Policy_subcategory == "Enforcement"  & grepl("urfew", Policy_description) ~ "Curfew" ,
    Policy_subcategory == "Enforcement"  & grepl("quarantine", Policy_description) ~ "Quarantine" ,
    Policy_subcategory == "Enforcement"  & grepl("isolation", Policy_description) ~ "Lockdown or Quarantine" ,
    Policy_subcategory == "Enforcement"  & grepl("stay at home|lockdown|Lockdown", Policy_description) ~ "Lockdown" ,
    Policy_subcategory == "Enforcement"  & grepl("test", Policy_description) ~ "Health Testing" ,
    Policy_subcategory == "Enforcement"  & grepl("travel|border closure", Policy_description) ~ "External Border Restrictions" ,
    Policy_subcategory == "Enforcement"  & grepl("Incorporates provinces of Alberta and Saskatchewan", Policy_description) ~ "New Task Force, Bureau or Administrative Configuration" ,
    Policy_subcategory == "Enforcement"  & grepl("road closure", Policy_description) ~ "Internal Border Restriction" ,
    Policy_subcategory == "Enforcement" ~ "Other Policy Not Listed Above",
    Policy_subcategory == "Courts" ~ "Other Policy Not Listed Above",
    is.na(Policy_subcategory) ~ "Other Policy Not Listed Above",
    TRUE ~ as.character(NA)
  ))%>%
  select(unique_id, type)
amp_coronanet_map = rows_patch(amp_coronanet_map, authorization, "unique_id")

3.9 Face mask

The following code maps how COVID AMP captures face mask policies to the CoronaNet taxonomy.

• The COVID AMP taxonomy aims to capture such policies by coding the Policy_category as Face mask

• The CoronaNet taxonomy aims to capture such policies by coding the type variable as Social Distancing. A more detailed mapping of to policy subtypes in the CoronaNet taxonomy would theoretically be possible but would not be possible to do easily. As such, it was not done in this initial mapping but rather reserved for downstream manual data harmonization.

mask = covidamp %>%
  filter(Policy_category == "Face mask") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type =  "Social Distancing"
  )%>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, mask, "unique_id")

3.10 Vaccine

The following code maps how COVID AMP captures vaccination policies to the CoronaNet taxonomy.

• The COVID AMP taxonomy aims to capture such policies by coding the Policy_category as Vaccinations

• The CoronaNet taxonomy aims to capture such policies by coding the type variable as COVID-19 Vaccine. In a section below, we further map sub types for COVID-19 vaccinations.

vaccine = covidamp %>%
  filter(Policy_category == "Vaccinations") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type = "COVID-19 Vaccines"
  )%>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, vaccine, "unique_id")

4.1 Business Restrictions

The following code maps how COVID AMP captures policies regarding business restrictions to the CoronaNet taxonomy despite the fact that it cannot be directly done.

• The following code extracts keywords from the Policy_subtarget variable in the COVID AMP taxonomy to determine whether as a business should be mapped as a Non-essential business or an Essential business in the business_cat variable. These keywords were chosen based on a manual inspection of the COVID AMP data.

• The following code extracts keywords from the Policy_subtarget variable in the COVID AMP taxonomy to determine what type of business is being targeted into the type_sub_cat variable. These keywords were chosen based on a manual inspection of the COVID AMP data.

temp = amp_coronanet_map %>%
  filter(type == "Restriction and Regulation of Businesses") %>%
  select(unique_id)

temp = covidamp[covidamp$unique_id %in% temp$unique_id,]
business_cat = temp %>%
  select(unique_id, Policy_subtarget) %>%
  mutate(
    non_essential_dum = ifelse(grepl("Non essential business", Policy_subtarget, ignore.case = F),
                               "Non-Essential Businesses", 
                               NA ),
    essential_dum = ifelse(grepl("Essential business", Policy_subtarget, ignore.case = F),
                           "Essential Businesses", NA )) %>%
  unite(institution_cat, 
        essential_dum, 
        non_essential_dum, na.rm = TRUE, sep = ',' ) %>%
  select(-c(2))
amp_coronanet_map = rows_patch(amp_coronanet_map, business_cat, "unique_id")


business_type = temp %>%
  select(unique_id, Policy_subtarget) %>%
  mutate(
    grocery_dum = ifelse(grepl("Grocery store", Policy_subtarget, ignore.case = F), "Supermarkets/grocery stores", NA ),
    hotel_dum = ifelse(grepl("Hotels/lodging", Policy_subtarget, ignore.case = F), "Paid lodgings (e.g. hotels)", NA ),
    personal_dum = ifelse(grepl("Personal care facilities", Policy_subtarget, ignore.case = F), "Personal Grooming Businesses (e.g. hair salons)", NA ),
    pharmacy_dum = ifelse(grepl("Pharmacy", Policy_subtarget, ignore.case = F), "Pharmacies", NA ),
    rest_dum = ifelse(grepl("Restaurants/bars", Policy_subtarget, ignore.case = F), "Restaurants,Bars", NA ),
    retail_dum = ifelse(grepl("Retail stores (not including food)", Policy_subtarget, ignore.case = F), "Retail Businesses", NA ),
    farm_dum  = ifelse(grepl("Farming/Agriculture/Food processing", Policy_subtarget, ignore.case = F), "Agriculture, forestry and fishing", NA)) %>%
  unite(type_sub_cat, grocery_dum, hotel_dum, personal_dum, pharmacy_dum, rest_dum, retail_dum, farm_dum, na.rm = TRUE, sep = ',' ) %>%
  select(-c(2))

amp_coronanet_map = rows_patch(amp_coronanet_map, business_type, "unique_id")

4.2 Schools

The following code maps how COVID AMP captures school closures/ restrictions to the CoronaNet taxonomy.

• COVID AMP and CoronaNet document whether a policy is targeted toward preschools, primary schools, secondary schools and higher eduction institutions but they use slightly different language to do so. The following extracts this information from the Policy_subtarget variable in the COVID AMP taxonomy to determine what school type is being targeted into the type_sub_cat variable. These keywords were chosen based on a manual inspection of the COVID AMP data.

temp = amp_coronanet_map %>%
  filter(type == "Closure and Regulation of Schools") %>%
  select(unique_id)
temp = covidamp[covidamp$unique_id %in% temp$unique_id,]
school_type = temp %>%
  select(unique_id, Policy_subtarget) %>%
  mutate(
    nurs_dum = ifelse(grepl("Childcare facilities (preschool or other daycare)", Policy_subtarget, ignore.case = F), "Preschool or childcare facilities (generally for children ages 5 and below)", NA ),
    prim_dum = ifelse(grepl("Primary education", Policy_subtarget, ignore.case = F), "Primary Schools (generally for children ages 10 and below)", NA ),
    sec_dum = ifelse(grepl("Secondary education", Policy_subtarget, ignore.case = F), "Secondary Schools (generally for children ages 10 to 18)", NA ),
    high_dum = ifelse(grepl("Higher education", Policy_subtarget, ignore.case = F), "Higher education institutions (i.e. degree granting institutions)", NA )) %>%
  unite(type_sub_cat, nurs_dum, prim_dum, sec_dum, high_dum, na.rm = TRUE, sep = ',' ) %>%
  select(-c(2))
amp_coronanet_map = rows_patch(amp_coronanet_map, school_type, "unique_id")

4.3 Mass Gatherings

The following code maps how COVID AMP captures restrictions of mass gathering policies to the CoronaNet taxonomy.

• COVID AMP and CoronaNet document whether a policy is targeted toward places of worship, prisons, and funeral homes but they use slightly different language to do so. The following extracts this information from the Policy_subtarget variable in the COVID AMP taxonomy to determine what gathering type is being targeted into the type_sub_cat variable in the CoronaNet taxonom. These keywords were chosen based on a manual inspection of the COVID AMP data.

temp = amp_coronanet_map %>%
  filter(type == "Restrictions of Mass Gatherings") %>%
  select(unique_id)
temp = covidamp[covidamp$unique_id %in% temp$unique_id,]
mass_type = temp %>%
  select(unique_id, Policy_subtarget) %>%
  mutate(
    relig_dum = ifelse(grepl("Churches/places of worship", Policy_subtarget, ignore.case = F), "Attendance at religious services restricted (e.g. mosque/church closings)", NA ),
    pris_dum = ifelse(grepl("Criminal justice system/prisons/jails/incarcerated persons", Policy_subtarget, ignore.case = F), "Prison population reduced (e.g. early release of prisoners)", NA ),
    fun_dum = ifelse(grepl("Funeral home/mortuary", Policy_subtarget, ignore.case = F), "Funerals and mourning rituals", NA )) %>%
  unite(type_sub_cat, relig_dum, pris_dum, fun_dum, na.rm = T, sep = ',' ) %>%
  select(-c(2))
amp_coronanet_map = rows_patch(amp_coronanet_map, mass_type, "unique_id") 

4.4 Vaccines

The following code maps how COVID AMP captures vaccination policies to the CoronaNet taxonomy in more detail than the initial mapping shown above.

• COVID AMP and CoronaNet document whether a policy is targeted the administration, distribution, logistics and prioritization of COVID-19 vaccines but they use slightly different language to do so. The following maps this information from Policy_subtarget variable in the COVID AMP taxonomy into the type_sub_cat variable in the CoronaNet taxonomy.

vaccine_sub = covidamp %>%
  filter(Policy_category == "Vaccinations") %>%
  select(unique_id, Policy_category, Policy_subcategory) %>%
  mutate(type_sub_cat = case_when(
    Policy_subcategory == "Vaccine administration, distribution, and logistics" ~ "Distribution of COVID-19 vaccines",
    Policy_subcategory == 'Vaccine prioritization' ~ "Distribution of COVID-19 vaccines",
    TRUE ~ as.character(NA)
  ))%>%
  select(-c(2,3))
amp_coronanet_map = rows_patch(amp_coronanet_map, vaccine, "unique_id")