Last update
Date: 28 September, 2024
Author: Emmanuel Kirui Barkacha

Outline

  1. Introduction

  2. Problem Statement and Objectives

  3. Study Area

  4. Required Data and Analysis

Introduction

  • Malaria remains a significant global health problem, particularly in sub-Saharan Africa.

  • In Kenya, an estimated 27 million malaria cases and 12,600 deaths were reported in 2020.

  • Malaria risk in Kenya is heterogeneous, and its epidemiology is influenced mainly by

  1. Altitude

  2. Rainfall patterns and temperature

  3. Previous interventions

  • Kenya has five malaria zones:Coastal endemic, Lake endemic, Seasonal transmission, Highlands epidemic and Low epidemic

    • Lake and coastal zones have year round transmission.

    • Highland and arid zones experience seasonal and epidemic malaria.

  • Kakamega county is classified under the lake endemic zone.

  • It experience intense malaria transmission year-round due to its favorable climatic conditions.

Problem Statement

Despite substantial progress in reducing malaria morbidity and mortality in Kenya, the disease remains a significant public health problem, particularly in western regions such as Kakamega County, where malaria transmission is intense and highly heterogeneity. Traditional malaria control strategies face challenges due to varying local epidemiological and environmental conditions. While mathematical models like OpenMalaria which is an individual based model offer valuable insights for predicting the impact of interventions, they require accurate calibration using local data to be effective. Currently, there is a lack of enough detailed, locally calibrated models for malaria transmission in high-prevalence and high heterogeneity areas like Kakamega County, which limits the effectiveness of targeted interventions and policy decisions.

Objectives

General Objective.

To calibrate the OpenMalaria model at admin 1 (county) level in a high spatial aggregation setting in Kakamega County.

Specific Objectives.

  1. To assess the additional value of calibrating the OpenMalaria model at admin 2 (sub-county) vs admin 1 (county).

  2. To assess the spatial variability of malaria transmission within Kakamega County, identifying high-risk areas and factors contributing to this variability.

  3. To evaluate the impact of the difference between the two calibrated models on the simulation of future interventions.

Study Area

Admin 1: Kakamega County

Kakamega County is located in the western region of Kenya. It is characterized by a diverse range of geographical and ecological features that make it a unique area for studying malaria transmission and control. The county lies between latitudes 0°07’30.0”N and 0°16’30.0”N and longitudes 34°32’30.0”E and 34°57’30.0”E. Kakamega is bordered by Vihiga County to the south, Siaya County to the west, Bungoma County to the north, and Nandi County to the east. The county covers an area of approximately 3,051.3 square kilometers.

Admin 2 : Kakamega Sub-Counties

The county is divided into 12 sub-counties: Lugari, Likuyani, Malava, Lurambi, Navakholo, Mumias West, Mumias East, Matungu, Butere, Khwisero, Shinyalu, and Ikolomani. Each sub-county exhibits unique demographic, socioeconomic, and environmental characteristics that influence malaria transmission dynamics.

Number of sub counties: 12

The table below shows the size of each sub-county from the smallest to the largest. Shinyalu is the largest Sub-county of the twelve with an approximate area of 445.5 Km2 whereas Ikolomani is the smallest with an approximate area of 143.6 Km2.

Area Per Sub-County
SubCounty Area_Km2
Ikolomani 143.6
Khwisero 145.6
Mumias East 149.2
Lurambi 161.7
Mumias West 165.3
Butere 210.4
Navakholo 258.0
Matungu 275.8
Likuyani 302.0
Lugari 367.0
Malava 427.2
Shinyalu 445.5
County 3,051.3

Climate

Kakamega County experiences a tropical rainforest climate, classified as Af under the Koppen climate classification. The county receives high annual rainfall, ranging from 1,280.1 mm to 2,214.1 mm, with two peak rainy seasons: the long rains from March to July and the short rains from December and February. The high rainfall and moderate temperatures, which average between 18°C and 29°C, create ideal conditions for the proliferation of Anopheles mosquitoes, the primary vectors of malaria. The county has also an average humidity of 67 per cent.

Historical Precipitations

For the precipitation data we are using the chirps library to load it for Kakamega county for the period \(1^{st} \text{ January } 2010\) to \(31^{st} \text{ December } 2023\) coming from Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) CHIRPS. This data from precipitation will be used as seasonality data as malaria transmission and precipitation are correlated. Since the chirps data is per day we will aggregate it to per month for each sub-county.

Demographic Profile

According to the 2019 Kenya Population and Housing Census Report by Kenya National Bureau of Statistics KNBS, the County’s population was 1,867,579 persons comprising of 897,133 males and 970,406 females, representing 48 percent male and 52 percent female. Malava Sub-County had the highest population of 238,330 persons while Shinyalu Sub County had the lowest population at 111,743 persons. This makes the County the fourth most populous county after Nairobi, Kiambu and Nakuru counties.

Population Distribution per Subcounty

SubCounty Male Female Total
BUTERE 73,634 80,463 154,097
IKOLOMANI 80,853 86,784 167,637
KHWISERO 53,670 59,803 113,473
LIKUYANI 73,710 78,341 152,051
LUGARI 90,884 98,016 188,900
LURAMBI 92,774 95,432 188,206
MALAVA 115,511 122,814 238,325
MATUNGU 78,793 88,143 166,936
MUMIAS EAST 55,895 60,953 116,848
MUMIAS WEST 54,915 60,438 115,353
NAVAKHOLO 73,275 80,695 153,970
SHINYALU 53,219 58,524 111,743
KAKAMEGA 897,133 970,406 1,867,539

Population Pyramid

Age Group Distribution

Population Projection (2024-2030)

For population projection we use the data from 2019 general knbs census and data from Kakaamega County Annual Development Plan (CADP) 2024/25. From the projection we can use the concept of exponential growth with the following formula

  1. To calculate the annual growth rate

The annual growth rate \(r\) between two years can be calculated using the formula:

\[ \text{r} = \left( \frac{\text{P}_{\text{end}}}{\text{P}_{\text{start}}} \right)^{\frac{1}{n}} - 1 \]

where:

  • \(\text{P}_{\text{end}}\) = The population at the end of the year

  • \(\text{P}_{\text{start}}\) = The population at the start of the year

  • \(n\) = The number of years between the start and end year

  1. To calculate Projected population

Once we know the growth rate from above , we can project the population for any future year using the formula below.

\[ P_{\text{future}} = P_{\text{present}} \times (1 + r)^{t}\]

where:

  • \(P_{\text{future}}\) is the projected population.

  • \(P_{\text{present}}\) is the population at the base year (2019 in this case).

  • \(r\) is the annual growth rate.

  • \(t\) is the number of years from the present year to the future year.

By using these formulas, we can make informed projections about how Kakamega County’s population will change for our case from 2024 to 2030. The annual growth rate gives us a sense of the rate of growth, and the projection formula allows us to estimate future populations based on this growth rate.

Projected Population Data for Kakamega County (2024-2030)
Region Base_2019 2024 2025 2026 2027 2028 2029 2030 AGR
Kakamega 1,867,579 2,036,901 2,072,565 2,108,853 2,145,777 2,183,347 2,221,575 2,260,472 0.0175089
Butere 154,100 168,071 171,014 174,008 177,055 180,155 183,309 186,519 0.0175088
Lurambi 188,212 205,276 208,870 212,527 216,248 220,034 223,887 227,807 0.0175087
Ikolomani 167,641 182,840 186,041 189,298 192,613 195,985 199,416 202,908 0.0175086
Malava 238,330 259,938 264,489 269,120 273,832 278,626 283,505 288,468 0.0175087
Shinyalu 111,743 121,874 124,008 126,179 128,389 130,636 132,924 135,251 0.0175089
Khwisero 113,476 123,764 125,931 128,136 130,379 132,662 134,985 137,348 0.0175086
Likuyani 152,055 165,841 168,745 171,700 174,706 177,765 180,877 184,044 0.0175093
Lugari 188,900 206,041 209,651 213,325 217,063 220,866 224,736 228,675 0.0175228
Matungu 166,940 182,075 185,263 188,507 191,807 195,165 198,582 202,059 0.0175085
Mumias East 116,851 127,446 129,677 131,948 134,258 136,609 139,001 141,434 0.0175094
Mumias West 115,354 125,812 128,015 130,256 132,537 134,857 137,219 139,621 0.0175085
Navakholo 153,977 167,938 170,878 173,870 176,914 180,012 183,164 186,371 0.0175093

Malaria Prevalence

I received the Plasmodium falciparum Parasite Rate (PfPR) data from Malaria Atlas Project (MAP) but it was provided by Swiss TPH team. The Plasmodium falciparum parasite rate (PfPR) is a commonly reported index of malaria transmission intensity. In our case we will be using PfPR as our malaria prevalence. The data received is of admin 2 from 1980 to 2024 which is labled 00_PfPR_table_Africa_CHAI_admin2_1980-2024.csv.The PfPR data contains PfPR_rmean(mean), PfPR_LCI (Lower Confidence Interval), PfPR_median (Median) and PfPR_UCI (Confidence Interval) which is for the age group 2-10 years.

For my analysis I subset the data to Kakamega subcounties for the year 2022.

Name Year Age Pop PfPR_rmean PfPR_LCI PfPR_median PfPR_UCI
Likuyani 2022 2-10_years 264921.4 0.0229792 0.0089153 0.0208298 0.0518890
Butere 2022 2-10_years 248916.6 0.1320386 0.0695961 0.1316295 0.2166028
Shinyalu 2022 2-10_years 190349.9 0.0789242 0.0405881 0.0775125 0.1348691
Khwisero 2022 2-10_years 121061.9 0.1260298 0.0621587 0.1271901 0.2102835
Lugari 2022 2-10_years 203342.0 0.0292948 0.0119859 0.0270091 0.0638100
Lurambi 2022 2-10_years 288374.5 0.0644496 0.0327343 0.0630284 0.1080119
Malava 2022 2-10_years 395393.0 0.0477680 0.0220321 0.0441859 0.0955322
Matungu 2022 2-10_years 211416.4 0.1334700 0.0606450 0.1296337 0.2404565
Mumias East 2022 2-10_years 142932.6 0.1036357 0.0526933 0.0978843 0.1763287
Mumias West 2022 2-10_years 208798.7 0.1320427 0.0643413 0.1274528 0.2279786
Navakholo 2022 2-10_years 252341.1 0.0755592 0.0364110 0.0715479 0.1448511
Ikolomani 2022 2-10_years 282693.4 0.0501423 0.0244225 0.0470628 0.0908116

Malaria Heterogeneity

We can see from the plot bellow that though the all 12 sub county are in Kakamega but we can see each one of them is a having a different PfPR.

The Plasmodium falciparum Parasite Rate (PfPR) map represent an annual average of PfPR in 2022.

Interventions

From the data I received we can observe that we have four interventions:

  • Insecticide-Treated Net (ITN)

  • Indoor Residual Spraying (IRS)

  • Seasonal Malaria Chemoprevention (SMC)

  • Artemisinin-based Monotherapy (AM)

I also had Data on malaria vaccine for some sub-counties in Kakamega. The data was from a website 25 More Sub-Counties in Western Kenya Receive Malaria Vaccine and a published paperMalaria vaccine coverage estimation using age-eligible populations and service user denominators in Kenya

Insecticide-Treated Net (ITN)

The ITN data is labeled 00_ITN_table_Africa_CHAI_admin2_1980-2024.csv. The data showed the mean ITN coverage rate for Admin2 which ranges from 1980 to 2024 for the all age groups.

Indoor Residual Spraying (IRS)

The IRS data is labeled 00_IRS_table_Africa_CHAI_admin2_1980-2024.csv. The data showed the mean IRS coverage rate for Admin2 which ranges from 1980 to 2024 for the all age groups. We can see from the data that 2014 was the last time IRS was used ever since the the IRS coverage is zero.

Seasonal Malaria Chemoprevention (SMC)

The SMC data is labeled 00_SMC_table_Africa_CHAI_admin2_1980-2024.csv. The data showed the mean SMC coverage rate for Admin2 which ranges from 1980 to 2024 for the all age groups. We can see from the data that there have never been SMC in Kakamega County.

Artemisinin-based Monotherapy (AM)

The Artemisinin-based Monotherapy data is labeled 00_AM_table_Africa_CHAI_admin2_1980-2024.csv. The data showed the mean AM coverage rate for Admin2 which ranges from 1980 to 2024 for the all age groups. In our case we will use AM to show the access to care rates Which we can see its 0.603466 in each of the subcounty. Since Artemisinin-based combination therapies are recommended as first line treatments for Plasmodium falciparum malaria we are going to use the AM as the effective treatment coverage.

Malaria Vaccine

The pilot RTS,S/AS01 malaria vaccine implementation programme in Kenya was undertaken in eight counties in the Western region: Bungoma, Busia, Homa Bay, Kakamega, Kisumu, Migori, Siaya, and Vihiga. In Kakamega county the subcounties which was targeted are Mumias West,Butere,Khwisero,Navakholo and Malava. Since the vaccine has four doses we are going to us the coverage of the first dose only which was estimated to be 80% in the western region.

Vector Distribution

Malaria vector distribution is the geographic and environmental spread of different Anopheles species. The vectors thrive in diverse environment influenced by different environmental factors such as amount temperature, humidity, rainfall, and the availability of breeding sites. The distribution of these vectors is closely linked to the transmission intensity of malaria.

The three common species which we will use in our research are Anopheles arabiensis, Anopheles_funestus and Anopheles gambiae ss which are distributed differently in the twelve sub-couties in Kakamega county. The spacial data we are using was from MAP but shared by Swiss TPH.