Dear Reader,
We are pleased to share our 5th weekly epidemiological bulletin for the year 2022.
This epidemiological bulletin serves to inform all stakeholders at district, national, and global levels on disease trends, public health surveillance and interventions undertaken in detecting, preventing and responding to public health events in Uganda on a weekly basis.
Data for this issue is from DHIS2
In this issue, we showcase the following updates:
Routine Surveillance
Reported suspected epidemic prone diseases
Reporting rates across the country -Public health emergencies in Uganda
Public health emergencies in neighboring countries
For comments please contact:
Dr. Allan Muruta,
Commissioner, Integrated Epidemiology, Surveillance and Public Health Emergencies - MoH;
P.O BOX 7272 Kampala, Tel: 080010066 (toll free); Email: links or esduganda22@gmail.com
Routine Surveillance
Fig 1: Districts Reporting highest Malaria upsurge
## # A tibble: 7 x 8
## yr average quant50 quant75 quant95 quant98 quant99 maxi
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 2016 3459. 1758 2907. 6142. 10556. 27414. 618102
## 2 2017 2972. 1582 2648 5843. 9258. 28277. 1366581
## 3 2018 24149. 1262. 2184. 4522. 9460. 31346. 151044503
## 4 2019 3619. 1876. 3365 7240. 11020. 15091. 2989975
## 5 2020 2346. 1788 3083. 5996 7955. 9353. 139209
## 6 2021 2262. 1818. 2872. 5810. 7555. 9167. 140507
## 7 2022 3568. 1631 2622. 5676. 7914. 9951. 428156
## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\DSN\Documents\R\Assigment\shape", layer: "ds"
## with 128 features
## It has 13 fields
## Integer64 fields read as strings: dc2017 Male Female PopnRtn TotalPopn Popn id## OGR data source with driver: ESRI Shapefile
## Source: "C:\Users\DSN\Documents\R\Assigment\shape", layer: "lk"
## with 86 features
## It has 5 fieldsTab 1: Suspected cases of epidemic prone diseases
| Condition | cases | Deaths | CFR |
|---|---|---|---|
| Acute_Flaccid_Paralysis_Cases2 | 319 | 1 | 0.00 |
| AEFI_Cases2 | 416 | 3 | 0.01 |
| Animal_bites_Suspected_rabies_Cases2 | 17665 | 32 | 0.00 |
| Anthrax_Cases2 | 57 | 4 | 0.07 |
| Bacterial_Meningitis_Cases2 | 208 | 15 | 0.07 |
| Cholera_Cases2 | 84 | 6 | 0.07 |
| Dysentery_Cases2 | 7242 | 269 | 0.04 |
| Guinea_Worm_Cases2 | 44 | 3 | 0.07 |
| Hepatitis_B_Cases2 | 6100 | 82 | 0.01 |
| Influenza_like_illness_Cases2 | 1679 | 158 | 0.09 |
| Leprosy_Cases2 | 134 | 9 | 0.07 |
| Malaria_diagnosed_Cases2 | 9214323 | 26972 | 0.00 |
| Measles_Cases2 | 2268 | 23 | 0.01 |
| Other_VHF_Cases2 | 207 | 7 | 0.03 |
| Plague_Cases2 | 66 | 4 | 0.06 |
| RR_TBcases_Cases2 | 132 | 3 | 0.02 |
| SARI_Cases2 | 14305 | 227 | 0.02 |
| SARS_Cases2 | 2367 | 240 | 0.10 |
| Typhoid_Fever_Cases2 | 84056 | 156 | 0.00 |
| Yellow_Fever_Cases2 | 114 | 36942 | 324.05 |
AFP-Acute Flaccid Paralysis; AEFIs-Adverse Events Following Immunization; NNT-Neonatal Tetanus; VHFF-Viral Hemorrhagic Fever; SARI-Severe Acute Respiratory Illness; RR T.B-Rifampicin Resistant Tuberculosis
Table 2: Maternal and Perinatal Deaths
| District | Maternal Deaths | District | macerated Deaths | District | Fresh Still Birth | District | Early Neonetal Deaths |
|---|---|---|---|---|---|---|---|
| Arua | 1 | Kampala | 23 | Kampala | 15 | Kampala | 35 |
| Gulu | 1 | Lira | 7 | Gulu | 7 | Mbarara | 7 |
| Hoima | 1 | Isingiro | 5 | Ibanda | 3 | Hoima | 6 |
| Kampala | 1 | Fort | 4 | Kabale | 3 | Gulu | 5 |
| Kasese | 1 | Gulu | 4 | Kamuli | 3 | Kabale | 4 |
| Koboko | 1 | Buikwe | 3 | Kasese | 3 | Kasese | 4 |
| Luwero | 1 | Ibanda | 3 | Kikuube | 3 | Masaka | 3 |
| Nakaseke | 1 | Kamuli | 3 | Mayuge | 3 | Fort | 2 |
| Sembabule | 1 | Kasese | 3 | Mbarara | 3 | Iganga | 2 |
| Abim | NA | Kitgum | 3 | Buikwe | 2 | Kamwenge | 2 |
We continue to register high maternal and perinatal deaths.
We recommend strengthening and continuity of community health services. In addition, regular reviews should be conducted with a focus on identifying modifiable factors behind these deaths
Figure 2: Trends of maternal and perinatal deaths Wk 5
WEEKLY SURVEILLANCE REPORTING RATES
Figure 3: Regional Surveillance Reporting Rates
Red - On time Completeness | Blue - Completeness
Completeness and timeliness of reporting is better compared to epi week 4;
8(53%) regions achieved the National Target of 80% for completeness and none for timeliness.
We recommend that Biostatisticians work with their DHTs to identify and address bottlenecks to reporting
AFP Surveillance
A total of 15 AFP case was reported
from the districts of Budaka 1, Bududa 2,
Bugiri 1, Bukedea 1, Butebo 1,
Kaberamaido 1, Kampala 1,
Katakwi 1, Kibaale 1, Kyegegwa 1,
Kyenjojo 1, Luuka 1, Nakeseke 1
and Nebbi 1 .
The adequate samples collection rate is 91.97%
compared with 92.29% in 2020 The Non-polio AFP rate is 521/100,000 children
0 - 14 years compared with 1.86/100,000 children 0 -14 years in 2020 NPENT rate is 9.38% compared with NPENT rate of 12.64% in 2020
Figure 4: Non Polio AFP rates by districts, Epi week 49
Public health emergencies in Uganda
COVID-19 Response activities
Incident Management Team meetings chaired by the Incident Commander continue every Monday, Wednesday and Friday.
Regional Support Teams deployed in June 2021 continue to support the COVID19 response in the health regions, bas-ing on a technical support plan for all pillars. The teams host daily situation update meetings with the national public health emergency operations center.
There is an ongoing nation-wide Accelerated Mass COVID-19 Vaccination Campaigns (AMVC) throughout the country
There are challenges that still need to be addressed:
Vaccination backlog data entry
Low reporting of alerts by the community
Non adherence to the COVID-19 SOPs
Inconsistent of reporting by districts on COVID19 situation through ODK
Figure 5: Uganda COVID-19 % test positivity rates/trends
Recommendations and acknowledgement
UNEPI reminds and encourages all the districts to carry out active search for AFP, NNT, AEFI and measles cases
District Health Officers should strengthen alert management and daily reporting of COVID-19 cases and deaths from all regions and health facilities.
MoH acknowledges efforts made by all districts and health facilities in surveillance activities
Editorial team
Dr Rutayisire Meddy
Remember, your feedback is important to us.
COVID –19 prevention is an individual responsibility. Be in charge! Be responsible! Take action!
R Script
knitr::opts_chunk$set(echo = FALSE,message = FALSE,
warning = FALSE)
library(readxl)
library(tidyverse)
library(ggplot2)
library(ggthemes)
library("tmap")
library("ggsci")
library("tmaptools")
library("sp")
library("rgdal")
library("here")
library(plotly)
old_16 <- read_excel("old_16.xls")
old_17 <- read_excel("old_17.xls")
old_18 <- read_excel("old_18.xls")
old_19 <- read_excel("old_19.xls")
new <- read_excel("dhis2_new.xls")
old <- rbind(old_16, old_17, old_18, old_19)
names(old) <- c("country",
"region",
"district",
"district2",
"period",
"opd_new",
"opd_tot",
"measles_cases",
"measles_dth",
"mic_pos",
"mic_test",
"rdt_pos",
"rdt_test",
"fever",
"mic_neg_t",
"mic_pos_t",
"not_test_t",
"rdt_pos_t",
"rdt_neg_t")
names(new) <- c("country",
"region",
"district",
"district2",
"period",
"opd_new",
"opd_tot",
"mal_cases",
"mal_dth",
"fever",
"rdt_test",
"rdt_pos",
"mic_test",
"mic_pos",
"not_test_t",
"rdt_neg_t",
"rdt_pos_t",
"mic_neg_t",
"mic_pos_t")
new_old <- old%>%
select(
region, district, period, opd_tot, fever,
rdt_test, rdt_pos, mic_test, mic_pos,
not_test_t, rdt_neg_t, rdt_pos_t, mic_neg_t, mic_pos_t
)
new_new <- new%>%
select(
region, district, period, opd_tot, fever,
rdt_test, rdt_pos, mic_test, mic_pos,
not_test_t, rdt_neg_t, rdt_pos_t, mic_neg_t, mic_pos_t
)
data_malaria <- rbind(new_old, new_new)
# data_malaria %>%
# filter(district == "Abim District" & period == "W1 2016")
new_old$data_age <- 0
new_new$data_age <- 1
data_malaria <- rbind(new_old, new_new)
data_malaria%<>%
separate(period, c("epi_week","year"),sep = " ",remove = FALSE)%>%
filter(data_age == 1 & year %in% c("2020", "2021", "2022")
|
data_age == 0 & year %in% c("2016", "2017", "2018", "2019")
)
data_c <- data_malaria%>%
mutate(
wk = as.integer(substring(epi_week,2)),
yr = as.integer(year))%>%
rowwise()%>%
mutate(
total_tested = sum(rdt_test,mic_test, na.rm = TRUE),
total_positive = sum(rdt_pos, mic_pos, na.rm = TRUE),
total_neg_treated = sum(mic_neg_t, rdt_neg_t, na.rm = TRUE),
total_pos_treated = sum(mic_pos_t, rdt_pos_t, na.rm = TRUE),
t_neg = total_positive-total_tested,
positivity_rate = 100*total_positive/total_tested,
dt = as.Date(paste(yr, wk, 1, sep = "-"), "%Y-%U-%u")
)
data_c %>%
group_by(yr) %>%
summarize(
average = mean(fever, na.rm=TRUE),
quant50 = quantile(fever, probs = .5 , na.rm=TRUE),
quant75 = quantile(fever, probs = .75, na.rm=TRUE),
quant95 = quantile(fever, probs = .95, na.rm=TRUE),
quant98 = quantile(fever, probs = .98, na.rm=TRUE),
quant99 = quantile(fever, probs = .99, na.rm=TRUE),
maxi = max(fever, na.rm = TRUE))
data_c$fever[ data_c$yr == 2018 & data_c$fever == 151044503 ] <- 31346
data_c$fever[data_c$yr == 2016 & data_c$fever > 10555] <- 10555
data_c$fever[data_c$yr == 2017 & data_c$fever > 9258] <- 9258
data_c$fever[data_c$yr == 2018 & data_c$fever > 9460] <- 9460
data_c$fever[data_c$yr == 2019 & data_c$fever > 11007] <- 11007
data_c$fever[data_c$yr == 2020 & data_c$fever > 9352] <- 9352
data_c$fever[data_c$yr == 2021 & data_c$fever > 9166] <- 9166
data_c$fever[data_c$yr == 2022 & data_c$fever > 9950] <- 9950
cData <- data_c %>%
select(district, wk, yr, total_positive) %>%
spread(key = yr,value = total_positive)
names(cData)[3:9] <- paste("Y", names(cData)[3:9], sep="")
x <- cData %>%
filter(wk != 53) %>%
rowwise() %>%
mutate(
UL_Mean = mean( c_across(Y2019:Y2021) ) + 2 * sd( c_across(Y2019:Y2021) ),
LL_Mean = mean( c_across(Y2019:Y2021) ) - 2 * sd( c_across(Y2019:Y2021) ),
UL_Medi = quantile( c_across(Y2019:Y2021), .75, na.rm=TRUE) ,
LL_Medi = quantile( c_across(Y2019:Y2021), .25, na.rm=TRUE)
)
x[ x$Y2022 == 0, c("Y2022") ] <- NA
u <- x %>%
filter(district == "Budaka District") %>%
ggplot(aes(wk)) +
geom_line( size=1,color="red" ,aes(y=UL_Medi)) +
geom_line( size =1, color = "green", aes(y = LL_Medi) ) +
geom_line( size=1,color="blue",aes(y=Y2022))+
geom_text(aes(x = 40,y=1800,label="Upper Limit",colour= "#ff4000"))+
geom_text(aes(x = 50,y=600,label="Lower Limit",colour= "black"))+
geom_text(aes(6,2300,label="current",colour="#0040ff"))+
theme_bw()+
scale_color_lancet ()+
theme(axis.text.y = element_text(size = 14, hjust = 1, family = "Fira Sans"),
plot.margin = margin(rep(15, 4)))+
labs(y="Malaria Cases",x="Epi Week",title = "Trend in Malaria Cases|Budaka")+
theme(
plot.title = element_text(color = "#294A5D", size = 25, face = "bold", hjust = 0.5))
u
t <- x %>%
filter(district == "Amolatar District") %>%
ggplot(aes(wk)) +
geom_line( size=1,color="red" ,aes(y=UL_Medi)) +
geom_line( size =1, color = "green", aes(y = LL_Medi) ) +
geom_line( size=1,color="blue",aes(y=Y2022))+
geom_text(aes(x = 45,y=1200,label="Upper Limit",colour= "Upper Limit"))+
geom_text(aes(x = 50,y=250,label="Lower Limit",colour= "Lower limit"))+
geom_text(aes(3,1200,label="current",colour="Current"))+
theme_bw()+
scale_color_lancet ()+
theme(axis.text.y = element_text(size = 14, hjust = 1, family = "Fira Sans"),
plot.margin = margin(rep(15, 4)))+
labs(y="Malaria Cases",x="Epi Week",title = "Trend in Malaria Cases|Amolatar")+
theme(
plot.title = element_text(color = "#294A5D", size = 25, face = "bold", hjust = 0.5))
t
#importing data
malaria <- read_excel("malaria.xls")
weekly_idsr <- read_excel("weekly idsr.xlsx")
weekly_cases_deaths <- read_excel("weekly cases deaths.xlsx")
weekly_EMTCT_malaria_TB_Stock <- read_excel("weekly EMTCT,malaria,TB,Stock.xlsx")
Reporting_rates <- read_excel("Report_rates.xls")
ds <- readOGR( here("shape"), "ds" )
lk <- readOGR( here("shape"), "lk" )
covid19 <- read_excel("covid19_daily_situation_report_Feb22.xlsx")
#renaming the data frames
names(malaria) <- c("orgleve1",
"region",
"district",
"org_name",
"period",
"opd_new",
"total_opd",
"malaria_cases",
"malaria_deaths",
"susp_malaria",
"cases_RDT",
"RDT_post",
"mic_tested",
"mic_positive",
"treated_not_tested",
"RDT_neg_treated",
"RDT_pos_treated",
"mic_neg_treated",
"mic_pos_treated")
names (weekly_cases_deaths) <- c("periodname_24",
"orgunitlevel124",
"region42",
"district24",
"organisationunitname24",
"Malaria_diagnosed_Cases2",
"Malaria_diagnosed_Deaths4",
"Dysentery_Cases2",
"Dysentery_Deaths4",
"SARI_Cases2",
"SARI_Deaths4",
"Acute_Flaccid_Paralysis_Cases2",
"Acute_Flaccid_Paralysis_Deaths4",
"AEFI_Cases2",
"AEFI_Deaths4",
"Animal_bites_Suspected_rabies_Cases2",
"Animal_bites_Suspected_rabies_Deaths4",
"Bacterial_Meningitis_Cases2",
"Bacterial_Meningitis_Deaths4",
"Cholera_Cases2",
"Cholera_Deaths4",
"Guinea_Worm_Cases2",
"Guinea_Worm_Deaths4",
"Measles_Cases2",
"Measles_Deaths4",
"Neonatal_tetanus_Cases",
"Neonatal_tetanus_Deaths",
"Plague_Cases2",
"Plague_Deaths4",
"Typhoid_Fever_Cases2",
"Typhoid_Fever_Deaths4",
"Typhoid_Fever_Cases_Tested",
"Typhoid_Fever_Cases_Positive",
"Hepatitis_B_Cases2",
"Hepatitis_B_Deaths4",
"Hepatitis_B_Cases Tested",
"Hepatitis_B_Cases Positive",
"RR_TBcases_Cases2",
"RR_TB_Death4s",
"RR_TBcases_Cases Tested",
"RR_TB_cases_Cases Positive",
"Yellow_Fever_Cases2",
"Yellow_Fever_Deaths4",
"Yellow_Fever_Cases_Tested",
"Yellow_Fever_Cases_Positive",
"Other_VHF_Cases2",
"Other_VHF_Deaths4",
"Other_VHF_Cases Tested",
"Other_VHF_Cases_Positive",
"Leprosy_Cases2",
"Leprosy_Deaths4",
"Leprosy_Tested_Cases",
"Leprosy_Positive_Cases",
"Anthrax_Cases2",
"Anthrax_Deaths4",
"Anthrax_Tested_Cases",
"Anthrax_Positive_Cases",
"Maternal_death_Deaths",
"Macerated_Still_births_Deaths",
"Fresh_Still_Birth_Deaths",
"Early_Neonatal_deaths",
"Other_Cases__Positive",
"Other_Cases_3_Total",
"Other_Cases_3_Deaths",
"Other_Cases_3_Tested",
"Other_Cases_3_Positive",
"Anthrax_human_Cases",
"Anthrax_human_Deaths",
"Lepros6y_Cases",
"Lepros6y_Deaths",
"Chikungunya_Cases",
"Chikungunya_Deaths",
"Dengue_Cases",
"Dengue_Deaths",
"Influenza_like_illness_Cases2",
"Influenza_like_illness_Deaths4",
"Acute_viral_hepatitis_Cases",
"Acute_viral_hepatitis_Deaths",
"Diarrhoea_with_dehydration_less_than_5_Cases",
"Diarrhoea_with_dehydration_less_than_5_deaths",
"Severe_pneumonia_less_than_5_Cases",
"Severe_pneumonia_less_than_5_Deaths",
"Human_African_Trypanosomiasis_Cases",
"Human_African_Trypanosomiasis_Deaths",
"Trachoma_Cases",
"Trachoma_Deaths",
"Schistosomiasis_Cases",
"Schistosomiasis_Deaths",
"Diphtheria_Cases",
"Diphtheria_Deaths",
"Pertussis_Whooping cough_Cases",
"Pertussis_Whooping cough_Deaths",
"Brucellosis_Cases",
"Brucellosis_Deaths",
"Kala_azar_Cases",
"Kala_azar_Deaths",
"Nodding_Syndrome_Cases",
"Nodding_Syndrome_Deaths",
"Adverse_Drug_Reactions_ADR_Cases",
"Adverse_Drug_Reactions_ADR_Deaths",
"Dracunculiasis_Cases",
"Dracunculiasis_Deaths",
"Onchocerciasis_Cases",
"Onchocerciasis_Deaths",
"Buruli_ulcer_Cases",
"Buruli_ulcer_Deaths",
"Lymphatic_Filariasis_Cases",
"Lymphatic_Filariasis_Deaths",
"Noma_Cases",
"Noma_Deaths",
"Human_influenza_due_to_a_new_subtype_Cases",
"Human_influenza_due_to_a_new_subtype_Deaths",
"SARS_Cases2",
"SARS_Deaths4",
"Smallpox_Cases",
"Smallpox_Deaths"
)
weekly_cases_deaths %<>%
separate(periodname_24, c("epi_week","year"),sep = " ",remove = FALSE)%>%
rowwise() %>%
mutate(wk = as.integer(substring(epi_week,2)),
yr = as.integer(year),
dt = as.Date(paste(yr, wk, 1, sep = "-"), "%Y-%U-%u"))
weekly_cases_deaths<- weekly_cases_deaths %>% separate(district24, c("district","b"),sep = " ",remove = TRUE)
weekly_cases<- weekly_cases_deaths %>%
select(contains("2"))
pp1 <- pivot_longer(data = weekly_cases, cols = Malaria_diagnosed_Cases2:SARS_Cases2, names_to = "Condition", values_to = "Cases") %>%
group_by(Condition) %>%
summarise(cases = sum(Cases, na.rm = TRUE))
weekly_deaths <- weekly_cases_deaths %>%
select(contains('4'))
pp2 <- pivot_longer(data = weekly_deaths, cols = Malaria_diagnosed_Deaths4:SARS_Deaths4, names_to = "Condition", values_to = "Deaths") %>%
group_by(Condition) %>%
summarise(value = sum(Deaths, na.rm = TRUE))
pp3 <- cbind(pp1,pp2)
pp1$Deaths <- pp2$value
pp1$CFR <- round(pp1$Deaths/pp1$cases,2)
maternal <- weekly_cases_deaths %>% select(
c(1,6,61,62,63,64)) %>%
filter(periodname_24 == "W4 2022") %>%
group_by(district)
T1 <- maternal %>% select(c(2,3))%>%
arrange(desc(Maternal_death_Deaths))%>%
head(10)
T2<- maternal %>% select(c(2,4))%>%
arrange(desc(Macerated_Still_births_Deaths))%>%
head(10)
T3 <- maternal %>% select(c(2,5))%>%
arrange(desc(Fresh_Still_Birth_Deaths))%>%
head(10)
T4 <- maternal %>% select(c(2,6))%>%
arrange(desc(Early_Neonatal_deaths))%>%
head(10)
maternal_deaths <- cbind(T1,T2,T3,T4)
library (knitr)
kable(pp1,align = "lccrr")
kable(maternal_deaths,align = "lccrr", col.names = c("District","Maternal Deaths","District","macerated Deaths","District","Fresh Still Birth","District","Early Neonetal Deaths"))
maternal_graph <- weekly_cases_deaths %>% select(
c(6,119,120,61,62,63,64)) %>%
mutate(
perinatal = sum(Macerated_Still_births_Deaths,Fresh_Still_Birth_Deaths,Early_Neonatal_deaths)
) %>%
transform( perinatal = as.integer(perinatal),
Maternal_death_Deaths = as.numeric(Maternal_death_Deaths))%>%
group_by(wk) %>%
summarise(en=sum(perinatal,na.rm = TRUE) , eng=sum(Maternal_death_Deaths,na.rm = TRUE)) %>%
ggplot(aes(wk))+
geom_line(aes(y = en,color = "perinatal"))+
geom_line(aes(y=eng,color = "Maternal"))+
theme_bw()+
theme(axis.text.y = element_text(size = 14, hjust = 1, family = "Fira Sans"),
plot.margin = margin(rep(15, 4)))+
labs(y="Deaths",x="")+
theme(
plot.title = element_text(color = "#294A5D", size = 25, face = "bold", hjust = 0.5))+
theme(legend.title = element_blank())+
scale_fill_manual(values= c("Perinatal", "Maternal"))
maternal_graph <- ggplotly(maternal_graph)
maternal_graph
mal_data <- malaria %>%
select(region,
district,
period,
malaria_cases,
malaria_deaths,
mic_tested,
cases_RDT,
RDT_post,
mic_positive)
#final datasets
data_mal <- mal_data%>%
separate(period, c("epi_week","year"),sep = " ",remove = FALSE)%>%
rowwise() %>%
mutate(wk = as.integer(substring(epi_week,2)),
yr = as.integer(year),
total_positive = sum(RDT_post, mic_positive, na.rm = TRUE),
dt = as.Date(paste(yr, wk, 1, sep = "-"), "%Y-%U-%u"))
names(Reporting_rates) <- c("region","week","completness","on_time")
Reporting_rates_c <- Reporting_rates %>%
separate(week, c("epi_week","year"),sep = " ",remove = FALSE)%>%
rowwise() %>%
mutate(wk = as.integer(substring(epi_week,2)),
yr = as.integer(year),
dt = as.Date(paste(yr, wk, 1, sep = "-"), "%Y-%U-%u"),
completness = round(completness,0),
on_time = round(on_time,0))%>%
filter(wk == 5)
Reporting_rates_c$region2 <- reorder(Reporting_rates_c$region,Reporting_rates_c$completness)
reporting_maps <- Reporting_rates_c%>% ggplot(aes(region2))+
geom_bar(aes(y=completness), stat="identity", width=0.9, binwidth=0,fill="#009999")+
geom_bar(aes(y=on_time), stat="identity", width=0.9, binwidth=0,fill="red")+
geom_hline(yintercept = 80,color="yellow")+
geom_text(aes(x = "Bunyoro",y=90,label="Target"))+
coord_flip()+
theme_bw()+
theme(axis.text.y = element_text(size = 14, hjust = 1, family = "Fira Sans"),
plot.margin = margin(rep(15, 4)))+
labs(y="Reporting Rate",x="")+
theme(
plot.title = element_text(color = "#294A5D", size = 25, face = "bold", hjust = 0.5))+
theme(legend.position = "bottom")
reporting_maps <- ggplotly(reporting_maps)
reporting_maps
pm <- tm_shape(ds) + tm_borders()
pm <- pm + tm_shape(lk) +
tm_polygons("#a6cee3" ) +
tm_borders("#a6cee3")
pm
covid192 <- covid19 %>% select(c(2,3,4,7,18,19,20,17,16,18,19,15))
names(covid192) <- c("date","region","district","alert_inv","alerts_pos","health","pcr","contact_pos","traveller_col","contact_col")
covid192$contact_col[covid192$contact_col == "n/a"] <- NA
covid192$pcr[covid192$pcr == "n/a"] <- NA
covid192 %<>% mutate(
cont_col = as.numeric(contact_col))%>%
mutate(
alert_pos_rate = round(alerts_pos/alert_inv,1),
cont_pos_rate = round(contact_pos/cont_col,1)
)
g <- covid192 %>%
group_by(date) %>%
summarise(en = sum(alert_pos_rate,na.rm =TRUE))%>%
ggplot(aes(date))+
geom_line(aes(y=en))+
theme_bw()+
scale_fill_aaas()+
theme(axis.text.y = element_text(size = 14, hjust = 1, family = "Fira Sans"),
plot.margin = margin(rep(15, 4)))+
labs(y="Positivity Rate",x="")
g