Malaria as an example of a parasitic disease spread by a vector (mosquito)

Pathogenic Diseases – Overview (Cambridge International AS & A Level Geography 9696)

Geographical definition of disease: a condition that affects the health of a population and whose spatial distribution is shaped by interactions between the pathogen, the host, and the environment. Geographers study where, when and why diseases occur, the impacts on societies and economies, and how control measures can be planned and evaluated.

1. Types of Pathogens

• Bacteria – single‑celled organisms (e.g., Mycobacterium tuberculosis).
• Viruses – acellular agents that require host cells to replicate (e.g., HIV, influenza).
• Fungi – yeasts and molds (e.g., Cryptococcus).
• Parasites – organisms that live on or in a host (protozoa, helminths). Malaria is the classic vector‑borne parasitic disease.

2. Main Transmission Routes

• Direct contact – skin‑to‑skin, sexual contact, mother‑to‑child.
• Indirect contact – contaminated food, water, fomites.
• Vector‑borne – transmission by living organisms such as insects or ticks.
• Airborne – droplets or aerosols carrying pathogens.

3. Impacts of Pathogenic Diseases (AO3)

Domain	Typical impacts
Health	morbidity, mortality, chronic disability.
Economic	loss of labour productivity, health‑care costs, reduced tourism and foreign investment.
Social	stigma, migration, changes in household structure, gendered care responsibilities.
Environmental	land‑use change that creates new breeding sites; feedbacks between disease control (e.g., insecticide use) and ecosystems.

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Malaria – A Vector‑Borne Parasitic Disease (AO2, AO3, AO4)

1. Disease Definition (Geographical perspective)

Malaria is a protozoan disease whose incidence is strongly linked to the spatial pattern of Anopheles mosquito habitats, climate suitability, and human vulnerability. Its distribution varies from local (household) to global scales, making it a key case study for the “Disease and Geography” global theme.

2. Causative Agents

Species	Geographic importance	Key features
P. falciparum	Sub‑Saharan Africa (≈90 % of deaths)	Most lethal; cerebral malaria possible.
P. vivax	South‑East Asia, Pacific, Americas	Hypnozoites → relapses.
P. malariae	Scattered worldwide	Low‑grade chronic infection.
P. ovale	Africa & Western Pacific	Similar to P. vivax with dormant liver stages.
P. knowlesi	Southeast Asia (zoonotic)	24‑hour replication cycle – rapid rise in parasitaemia.

3. Vector

• Only female Anopheles mosquitoes transmit malaria.
• Highly efficient species: An. gambiae, An. funestus, An. arabiensis.
• Peak biting: dusk to dawn; many species bite indoors (endophagic) and rest on walls (endophilic).

4. Life Cycle (Human ↔ Mosquito)

1. Human host – asexual phase
   • Infective sporozoites injected with mosquito saliva.
   • Travel to liver → develop into merozoites.
   • Merozoites invade red blood cells → fever‑chill cycles.
   • Some differentiate into male/female gametocytes (sexual stage).
2. Mosquito host – sexual phase
   • During a blood meal the mosquito ingests gametocytes.
   • In the gut gametocytes mature, fuse → zygote.
   • Zygote becomes an ookinete, penetrates gut wall, forms an oocyst.
   • Oocyst releases thousands of sporozoites that migrate to the salivary glands.

5. Geographic Distribution (Scale & Map Interpretation)

Malaria occurs where climate, water, and human factors create suitable conditions for Anopheles breeding and parasite development. Distribution can be examined at three scales:

• Local – household or village level (e.g., proximity to stagnant water, house construction).
• Regional – river basins, irrigated valleys, high‑risk districts.
• Global – endemic zones across continents.

Sample choropleth map description for exams:

• Colour‑graded risk classes based on mean annual temperature (20‑30 °C) and rainfall (>800 mm yr⁻¹).
• Overlay of population density highlights exposure hotspots.
• Land‑cover layer (wetlands, rice paddies) pinpoints breeding sites.

Historical change

19th‑century railway and colonial expansion introduced malaria into high‑land areas (e.g., Kenyan highlands). In the 21st century, climate warming has shifted the transmission altitude upward by ≈100 m per decade in East Africa.

6. Factors Influencing Spread (AO2 – cause‑and‑effect chains & quantitative links)

Factor	Cause‑and‑Effect Chain	Quantitative link (where applicable)
Climate	Higher temperature → faster parasite development (shorter extrinsic incubation period, EIP) → increased transmission potential.	EIP ≈ 14 days at 20 °C; ≈7 days at 28 °C.
Land‑use change	Deforestation / irrigation → creation of stagnant water → more larval habitats → higher vector density.	Each 10 % increase in irrigated area can raise mosquito density (m) by ~0.5‑1.0.
Poverty & housing	Poor housing (no screens) + limited net use → greater human‑mosquito contact.	Households without screens experience ~2‑3× higher bite rates.
Human movement	Labour migration → importation of parasites into low‑risk zones → potential for local transmission if vectors are present.	Seasonal migrant workers contributed to 12 % of new cases in the Kenyan highlands (2005‑2010).
Health‑system factors	Limited diagnostics → delayed treatment → higher parasite densities → increased gametocyte carriage → more infectious bites.	RDT coverage < 50 % correlates with a 1.8‑fold rise in incidence.
Social & cultural determinants	Gendered sleeping arrangements (men sleeping outdoors) → higher exposure for men; cultural beliefs discouraging net use.	Net use among women 68 % vs. men 54 % in rural Tanzania.

7. Impacts of Malaria (AO3 – structured evaluation framework)

When answering exam questions, organise impacts using the following framework:

1. Health impact – morbidity, mortality, long‑term disability, burden on health services.
2. Economic impact – loss of labour productivity, health‑care expenditure, reduced foreign investment.
3. Social impact – education disruption, gender roles, migration patterns.
4. Demographic impact – infant mortality, altered population growth, age‑structure changes.
5. Environmental impact – insecticide runoff, changes in water management, biodiversity loss.
6. Policy & development impact – constraints on infrastructure, achievement of SDG 3.

Evaluation criteria (exam‑style) to compare impacts:

• Magnitude (e.g., DALYs lost)
• Geographic reach (local vs. national)
• Duration (short‑term shock vs. chronic burden)
• Equity – which groups are most affected?

8. Control & Prevention Strategies (AO4 – evaluation of each measure)

1. Vector control
   • Insecticide‑treated nets (ITNs) – high coverage (>80 %) reduces bite exposure by 50‑60 %; low per‑person cost.
     Limitation: insecticide resistance, net durability, cultural misuse.
   • Indoor residual spraying (IRS) – kills resting mosquitoes; effective where vectors are endophilic.
     Limitation: high operational cost, need for repeated applications, resistance to pyrethroids.
   • Larval source management – drainage, intermittent irrigation, biological control (larvivorous fish, Bti).
     Limitation: labour‑intensive, requires community participation, may affect agriculture.
2. Case management
   • Rapid diagnosis (RDTs or microscopy) → prompt treatment.
   • Artemisinin‑based combination therapies (ACTs) – first‑line; reduces parasite clearance time.
   • Severe malaria: IV artesunate, blood transfusion, supportive care.
   • Limitation: drug resistance (e.g., artemisinin resistance in the Greater Mekong), supply chain gaps.
3. Vaccination

   RTS,S/AS01 (Mosquirix) – ~30 % efficacy against clinical disease; pilot programmes in Ghana, Kenya, Malawi.
   Limitation: modest protection, requires four‑dose schedule, cost.

4. Surveillance & community education
   • Routine reporting via DHIS2 or national HMIS – enables rapid outbreak detection.
   • Behaviour‑change campaigns to improve net use and treatment‑seeking.
   • Limitation: data quality issues, literacy barriers.
5. Policy frameworks
   • WHO Global Technical Strategy for Malaria 2016‑2030 (target: 90 % reduction in incidence, 95 % reduction in mortality).
   • National Malaria Control Programmes (NMCPs) – integrate vector control, case management, monitoring.
   • Evaluation: sustainability of funding, inter‑sectoral coordination, alignment with SDGs.

9. Quantitative Modelling – Basic Reproduction Number (R₀)

The basic reproduction number expresses the average number of secondary human cases generated by one infected person in a fully susceptible population:

\[ R_0 = \frac{m \, a^2 \, b \, c \, e^{-\mu_g T}}{\mu_g} \]

Symbol	Definition	Typical tropical range
m	Female mosquitoes per human	5–30
a	Bites per mosquito per day	0.3–0.5 day⁻¹
b	Transmission probability mosquito → human	0.10–0.20
c	Transmission probability human → mosquito	0.10–0.20
\(\mu_g\)	Adult mosquito daily mortality rate	0.10–0.20 day⁻¹
T	Extrinsic incubation period (days)	10–14 days (temperature‑dependent)

Worked example – effect of ITNs on R₀ (assume baseline values: m = 15, a = 0.4, b = 0.15, c = 0.15, \(\mu_g\) = 0.12, T = 12 days).

1. Calculate baseline R₀: \[ R_0 = \frac{15 \times 0.4^2 \times 0.15 \times 0.15 \times e^{-0.12 \times 12}}{0.12} \approx \frac{15 \times 0.16 \times 0.0225 \times e^{-1.44}}{0.12} \approx \frac{0.054 \times 0.236}{0.12} \approx 0.106 \] (R₀ > 1, transmission sustained.)
2. Introduce ITNs that reduce the biting rate (a) by 50 % → a = 0.2.
3. Re‑calculate: \[ R_0^{\text{ITN}} = \frac{15 \times 0.2^2 \times 0.15 \times 0.15 \times e^{-0.12 \times 12}}{0.12} \approx \frac{15 \times 0.04 \times 0.0225 \times 0.236}{0.12} \approx \frac{0.0032}{0.12} \approx 0.027 \] R₀ falls well below 1 – transmission would collapse.

Similar calculations can be performed for other parameters (e.g., reducing m through larval control or increasing \(\mu_g\) via IRS).

10. Key Statistics (2023)

Region	Estimated Cases (millions)	Estimated Deaths (thousands)	Primary Plasmodium Species
Sub‑Saharan Africa	215	620	P. falciparum
South‑East Asia	7	30	P. vivax, P. falciparum
Western Pacific	5	15	P. vivax
Americas	1	5	P. vivax

11. Case Studies (Two contrasting examples)

11.1 Kilombero Valley, Tanzania (2000‑2020)

Background: Low‑lying, high‑rainfall rice‑cultivation area; malaria incidence 312/1 000 in 2000.

• ITN distribution (school‑based, 80 % coverage by 2010).
• IRS with pyrethroids (2005‑2015) → rotation to organophosphates (2016) after resistance.
• Community health workers delivering RDT‑based diagnosis & ACTs.
• Larval management in rice paddies (intermittent irrigation, larvivorous fish).

Outcomes (2020):

• Incidence ↓ 75 % (312 → 78/1 000).
• Under‑5 mortality ↓ 62 % (120 → 45/1 000 live births).
• ITN usage 78 % of children.
• Emerging pyrethroid resistance → shift to pirimiphos‑methyl IRS.

Evaluation (exam‑style):

Criterion	Positive aspects	Limitations / challenges
Cost‑effectiveness	ITNs delivered through schools achieved low cost per DALY averted.	IRS required high recurrent funding; logistical complexity.
Sustainability	Community health workers built local capacity.	Dependence on donor‑funded net replacement cycles.
Equity	High net use among children; reduced gender gap.	Adult men working in fields remained less protected.
Resistance management	Timely switch to organophosphate IRS.	Limited monitoring capacity for insecticide resistance.

11.2 Sri Lanka – Malaria Elimination (1999‑2016)

Context: Island nation with diverse topography; historically endemic in low‑lying coastal zones.

• Intensive IRS (DDT → pyrethroids) combined with robust surveillance.
• Mass drug administration (MDA) in high‑risk districts.
• Strong political commitment and cross‑sectoral coordination (health, agriculture, tourism).
• Targeted larval source management in irrigation schemes.

Result: Zero indigenous cases reported in 2016; WHO certified malaria‑free status in 2018.

Key lessons for contrast:

• Small population and island geography facilitated rapid response.
• High government financing reduced reliance on external donors.
• Continuous post‑elimination surveillance is essential to prevent re‑introduction.

12. Key Geographical Concepts Embedded in the Malaria Topic

• Scale – local (household), regional (valley, district), global (endemic zones).
• Place – physical (climate, water bodies) and human (housing, land‑use) characteristics that make an area “malaria‑prone”.
• Spatial variation – why incidence differs between highland and lowland, urban vs. rural.
• Change over time – historical spread, recent altitude shifts, impact of interventions.
• Cause‑and‑effect – chains linking environment → vector → disease.
• Systems approach – interaction of biological, environmental, socio‑economic, and policy subsystems.
• Environmental interaction – how control measures affect ecosystems and vice‑versa.
• Challenges & opportunities – drug/insecticide resistance, climate change, funding.
• Diversity & equality – differential vulnerability by age, gender, income.

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