Add counts of individual cluster locations. Operates on the output list of
the compress_clusters() component. Calculates individual location
counts for each cluster, and appends to the cluster location list.
Examples
case_grid <- generate_case_grids(
example_count_data, example_count_data[, max(date)]
)
nci <- gen_nearby_case_info(
cg = case_grid,
distance_matrix = county_distance_matrix("OH")[["distance_matrix"]],
distance_limit = 25
)
obs_exp_grid <- generate_observed_expected(
nearby_counts = nci,
case_grid = case_grid
)
cla <- add_spline_threshold(oe_grid = obs_exp_grid)
# use compress clusters to reduce
cla <- compress_clusters_fast(
cluster_alert_table = cla,
distance_matrix = county_distance_matrix("OH")[["distance_matrix"]]
)
# Now add the location counts
add_location_counts(
cluster_list = cla,
cases = example_count_data
)
#> $cluster_alert_table
#> Key: <target>
#> target observed spl_thresh date test_totals location base_clust_sums
#> <char> <int> <num> <Date> <int> <char> <num>
#> 1: 39003 335 0.2113664 2025-01-30 9673 39003 1263
#> 2: 39005 166 0.3304438 2025-01-30 9673 39005 489
#> 3: 39009 215 0.2769749 2025-01-30 9673 39009 560
#> 4: 39015 80 0.4995393 2025-02-04 2858 39025 862
#> 5: 39017 280 0.2330866 2025-01-30 9673 39017 1383
#> 6: 39039 67 0.5758212 2025-02-01 6898 39039 335
#> 7: 39061 287 0.2297259 2025-02-02 5553 39061 2270
#> 8: 39081 37 0.8068677 2025-02-04 2858 39081 326
#> 9: 39109 399 0.1928263 2025-02-04 2858 39021 5866
#> 10: 39141 160 0.3385027 2025-01-31 8267 39129 794
#> distance_value count detect_date baseline_total expected log_obs_exp
#> <num> <int> <Date> <num> <num> <num>
#> 1: 0.00000 67 2025-02-05 63803 191.48001 0.5593471
#> 2: 0.00000 44 2025-02-05 63803 74.13597 0.8060870
#> 3: 0.00000 59 2025-02-05 63803 84.90008 0.9291630
#> 4: 17.32111 26 2025-02-05 63803 38.61254 0.7284495
#> 5: 0.00000 46 2025-02-05 63803 209.67288 0.2892410
#> 6: 0.00000 14 2025-02-05 63803 36.21820 0.6151308
#> 7: 0.00000 79 2025-02-05 63803 197.56610 0.3734090
#> 8: 0.00000 20 2025-02-05 63803 14.60289 0.9296987
#> 9: 24.96767 11 2025-02-05 63803 262.76238 0.4177113
#> 10: 22.46182 10 2025-02-05 63803 102.87914 0.4416189
#> min_dist alert_ratio alert_gap id nr_locs max_date count_sum
#> <num> <num> <num> <int> <int> <IDat> <int>
#> 1: 0.00000 2.646338 0.34798069 1 1 2025-02-05 335
#> 2: 0.00000 2.439407 0.47564318 8 1 2025-02-05 166
#> 3: 0.00000 3.354683 0.65218806 13 1 2025-02-05 215
#> 4: 17.32111 1.458243 0.22891024 28 2 2025-02-05 21
#> 5: 0.00000 1.240916 0.05615438 33 1 2025-02-05 280
#> 6: 0.00000 1.068267 0.03930963 49 1 2025-02-05 67
#> 7: 0.00000 1.625454 0.14368309 54 1 2025-02-05 287
#> 8: 0.00000 1.152232 0.12283098 62 1 2025-02-05 37
#> 9: 24.96767 2.166257 0.22488502 72 5 2025-02-05 36
#> 10: 22.46182 1.304625 0.10311621 111 3 2025-02-05 79
#>
#> $cluster_location_counts
#> location count target
#> <char> <int> <char>
#> 1: 39009 215 39009
#> 2: 39005 166 39005
#> 3: 39003 335 39003
#> 4: 39015 21 39015
#> 5: 39025 59 39015
#> 6: 39021 25 39109
#> 7: 39037 26 39109
#> 8: 39109 36 39109
#> 9: 39113 299 39109
#> 10: 39149 13 39109
#> 11: 39061 287 39061
#> 12: 39081 37 39081
#> 13: 39129 59 39141
#> 14: 39131 22 39141
#> 15: 39141 79 39141
#> 16: 39017 280 39017
#> 17: 39039 67 39039
#>
#> attr(,"class")
#> [1] "list" "clusters"