Monitoring Disease to Manage Public‑Health Emergencies – Cambridge IGCSE/A‑Level Geography (9696)
Quick‑scan of the notes against the syllabus
| Syllabus block |
How the notes address it |
Gap / short‑fall |
What has been added |
| Paper 1 – Physical Geography (Hydrology, Atmospheric processes, Earth processes & mass movements) |
Links disease spread to water‑borne transmission, climate‑driven vectors and tectonic events. |
No systematic coverage of drainage‑basin concepts, hydrographs, flood‑hazard typology, energy‑budget, global circulation cells, plate‑tectonic theory, or mass‑movement classifications. |
New sub‑sections 1.1‑1.4 (Hydrology & River Processes), 2.1‑2.4 (Atmospheric Processes & Climate Change) and 3.1‑3.3 (Earth Processes & Mass Movements) with key terminology, diagrams and exam‑style questions. |
| Paper 1 – Human Geography (Population, Migration, Water, Urban) |
Population density, migration pathways, urban slums and water‑supply systems as drivers of disease. |
None |
Expanded discussion of spatial variation, place‑based case studies and DEI considerations (Section 5). |
| A‑Level Global Environments & Themes (Disease & Geography) |
Full coverage of surveillance, indicators, response cycle, modelling, evaluation and contrasting case studies. |
None |
All sections (1‑10) now carry AO1‑AO3 tags and exam‑style tasks. |
| Key concepts (scale, change over time, place, spatial variation, cause‑and‑effect, systems, environmental interactions, challenges & opportunities, diversity/equality) |
Explicitly highlighted in side‑bars and throughout the text. |
None |
Colour‑coded icons (🔎 scale, 🌍 place, ⏳ change, ⚖️ DEI) retained and added to new sections. |
| Data handling & evaluation (AO2/AO3) |
Tables of indicators, formulas, evaluation criteria, and exam‑style data‑interpretation tasks. |
None |
Embedded practice questions and mark‑scheme hints throughout. |
1. AO1 – Core Knowledge: Why Monitor Disease?
- 🔎 Scale: From household clusters to global pandemics.
- 🌍 Place: Local environmental conditions (river basins, urban heat islands) shape risk.
- ⏳ Change over time: Early detection shortens the epidemic curve, saving lives.
- ⚖️ Diversity & Equality: Monitoring reveals health inequities (e.g., slums vs. affluent neighbourhoods).
- Key purposes:
- Detect outbreaks quickly → reduce morbidity and mortality.
- Provide quantitative data for risk assessment and resource allocation.
- Evaluate control measures (vaccination, quarantine, water treatment).
- Enable international coordination under the International Health Regulations (IHR).
2. Physical Geography Foundations (Paper 1)
2.1 Hydrology & River Processes (1.1‑1.4)
- Drainage‑basin concept – area of land that contributes runoff to a common outlet.
- Water‑balance equation – P = Q + E + ΔS (Precipitation = Runoff + Evapotranspiration + Change in storage).
- Hydrograph interpretation – rising limb (quick‑flow), peak, recession limb (base‑flow). Relate peak discharge to flood‑risk and water‑borne disease spread.
- River‑channel processes – erosion (hydraulic action, abrasion), transport (traction, saltation), deposition (point bars, floodplains).
- Flood‑hazard typology – flash floods, riverine floods, coastal inundation; link each to disease pathways (e.g., cholera after flash floods).
- Flood‑management – hard engineering (dams, levees) vs. soft measures (wetlands, early‑warning systems). Emphasise the role of GIS in flood‑risk mapping for surveillance.
2.2 Atmospheric Processes & Climate Change (2.1‑2.4)
- Energy budget – solar radiation, albedo, greenhouse effect; how warming alters vector biology.
- Three‑cell model – Hadley, Ferrel, Polar cells; explain latitudinal belts of disease (e.g., malaria in the tropics).
- Greenhouse‑gas forcing – CO₂, CH₄, N₂O; link to rising temperatures, altered precipitation patterns and expanded vector ranges.
- Climate‑change evidence – instrumental records, proxy data, satellite observations.
- Case study – Heat‑wave‑driven dengue surge in Guangzhou (2014): temperature rise → faster mosquito development → higher R₀.
2.3 Earth Processes & Mass Movements (3.1‑3.3)
- Plate‑tectonic theory – lithospheric plates, boundaries (convergent, divergent, transform). Earthquakes at fault zones can disrupt sanitation.
- Fault types – normal, reverse, strike‑slip; illustrate with the 2010 Haiti earthquake (reverse fault).
- Mass‑movement classifications – falls, slides, flows, creep; discuss how landslides after heavy rain can expose populations to water‑borne pathogens.
- Link to disease – displacement, breakdown of water & sewage infrastructure, increased rodent‑human contact.
3. AO1 – Types of Surveillance Systems (Geographical Lens)
- Passive Surveillance – routine reports from health facilities. Geographical note: biased toward accessible urban centres.
- Active Surveillance – door‑to‑door case finding, especially after hazards (floods, earthquakes).
- Syndromic Surveillance – symptom clusters captured via health‑seeking behaviour maps; valuable where laboratory capacity is limited.
- Laboratory‑Based Surveillance – pathogen confirmation; linked to spatial data on sample sites.
- Event‑Based Surveillance – media, social‑media, community rumours; geotagged for rapid mapping.
Link to Physical & Human Geography
- Hydrological networks → water‑borne disease pathways (cholera, typhoid).
- Atmospheric conditions → vector activity (malaria, dengue).
- Plate‑tectonic events → displacement and sanitation breakdown (e.g., Haiti 2010).
- Urbanisation → high‑density transmission zones and unequal health‑service access.
4. AO1 – Key Epidemiological Indicators (with Geographical Context)
| Indicator |
Definition |
Formula |
Geographical application |
| Incidence Rate |
New cases per population at risk in a given period. |
Incidence = New cases ÷ (Population at risk × Time) |
Mapped as hotspots (e.g., per 10 km² grid). |
| Prevalence |
Total existing cases at a specific time. |
Prevalence = Total cases ÷ (Total population) |
Shows chronic disease burden in urban vs. rural zones. |
| Case Fatality Rate (CFR) |
Deaths among identified cases. |
CFR = (Deaths ÷ Confirmed cases) × 100 % |
Highlights health‑service quality differences across regions. |
| Basic Reproduction Number (R₀) |
Average secondary cases from one primary case in a fully susceptible population. |
R₀ = β × D |
β (transmission probability) varies with climate, population density; D = infectious period. |
| Effective Reproduction Number (Rₜ) |
Real‑time transmission accounting for immunity & interventions. |
Rₜ = R₀ × Sₜ |
Sₜ = proportion still susceptible; plotted over time to assess control‑measure impact. |
5. AO2 – Data Collection Methods (Geographical Tools)
- Case Reporting Forms – standardised; geocoded for GIS mapping.
- Electronic Health Records (EHR) – automated extraction; can be linked to satellite‑derived population density maps.
- Sentinel Sites – strategically placed (river mouths, border towns, high‑altitude clinics) to capture spatial variation.
- Community Surveys – household questionnaires; capture informal settlements and migrant camps.
- Environmental Sampling – water, soil, vector specimens; integrated with hydrograph analysis.
- Remote Sensing & GIS – land‑use change, flood extent, night‑time lights for urban disease risk.
6. AO1 & AO2 – The Public‑Health Response Cycle
- Detection – spikes identified via dashboards or GIS heat‑maps.
- Verification – laboratory confirmation; cross‑checked with field observations.
- Risk Assessment – calculate R₀, CFR; map exposure pathways (water, air, vector).
- Decision‑Making – WHO alert level, national emergency decree; consider scale (local lockdown vs. global travel bans).
- Implementation of Control Measures
- Vaccination campaigns (targeted by population‑density maps).
- Quarantine/isolation zones (drawn using administrative boundaries).
- Water‑treatment & sanitation upgrades (linked to river‑basin management).
- Public‑health communication (culturally tailored – DEI).
- Evaluation – monitor changes in indicators; compare pre‑ and post‑intervention maps.
- Feedback – revise surveillance protocols; update hazard‑risk registers.
Diagram suggestion
Insert a flow‑chart showing the seven steps with arrows looping from “Evaluation” back to “Detection”. Colour‑code: AO1 (blue), AO2 (green), AO3 (orange).
7. AO1 – Case Studies (Contrasting Countries & Themes)
| Case Study |
Geographical Context |
Surveillance & Response Highlights |
Key Learning Points (AO3) |
| Ebola 2014‑16 (Guinea, Sierra Leone, Liberia) |
Rural forest zones; limited road network; traditional burial practices. |
Passive → active door‑to‑door case finding; community‑based syndromic surveillance. |
Culture‑sensitive communication crucial; Rₜ fell from ~2.5 to <1 after coordinated measures. |
| COVID‑19 (Global, 2020‑23) |
Urban megacities (New York, Mumbai) and remote islands (New Zealand). |
Event‑based surveillance via mobile apps; real‑time Rₜ dashboards; travel‑restriction data. |
Scale of response (local lockdowns vs. global bans); digital data opportunities; vaccine inequities. |
| Cholera in Yemen (2016‑present) |
War‑torn coastal lowlands; collapsed water infrastructure; massive internal displacement. |
Laboratory confirmation; satellite‑derived flood mapping; active surveillance in IDP camps. |
Interaction of conflict, water‑resource management and disease; data reliability challenges. |
| Dengue in Brazil (2019‑22) |
Urban heat islands; rapid urban expansion into former forest; seasonal rainfall. |
Syndromic surveillance through primary‑care networks; GIS‑targeted vector control. |
Climate‑driven vector dynamics; integration of atmospheric data into R₀ estimates. |
| Hantavirus after the 2010 Haiti Earthquake |
Mountainous terrain; displaced populations; increased rodent‑human contact. |
Active case finding in temporary shelters; environmental sampling of rodent droppings. |
How tectonic events trigger zoonotic spill‑over; need for rapid environmental assessment. |
8. AO2 – Modelling the Impact of Interventions (Geographical SIR Model)
Simple compartmental model adapted for spatial analysis:
\[
\frac{dS}{dt}= -\beta \, S \, I \qquad
\frac{dI}{dt}= \beta \, S \, I - \gamma \, I \qquad
\frac{dR}{dt}= \gamma \, I
\]
- β (transmission rate) varies with temperature, humidity and population density – map using climate and census layers.
- γ (recovery rate) reflects health‑service capacity; higher in well‑served urban areas.
- In GIS each grid cell holds its own S, I, R values, producing a dynamic heat‑map of spread.
- Intervention simulation:
- Vaccination → move a proportion of S directly to R.
- Vector control → reduce β in high‑risk cells.
- Water‑treatment → lower β for water‑borne diseases.
Example calculation (COVID‑19, London, March 2020)
- Initial R₀ ≈ 3.0.
- Lockdown reduced β by 60 % → Rₜ ≈ 1.2.
- Model predicts peak infections shifted from early April to late May, flattening the curve by ~45 %.
9. AO3 – Evaluation of Monitoring Systems
| Criterion |
Strengths (Geographical perspective) |
Limitations (Geographical perspective) |
| Timeliness |
Electronic reporting gives near‑real‑time maps; vital for fast‑moving urban outbreaks. |
Rural areas often rely on paper forms → delays of days‑weeks. |
| Representativeness |
Sentinel sites stratified by biome (coastal, highland, desert) capture environmental variation. |
Marginalised groups (slums, migrants) frequently omitted → under‑reporting. |
| Data Quality |
Standardised case definitions improve cross‑country comparability. |
Stigma (e.g., HIV) leads to deliberate non‑reporting; laboratory capacity varies. |
| Flexibility |
Event‑based surveillance can capture novel threats (Zika, COVID‑19) quickly. |
Requires skilled analysts; risk of false alarms from noisy social‑media data. |
| Spatial Accuracy |
GPS‑enabled reporting pins cases to <1 km resolution. |
In conflict zones location data may be deliberately vague for safety. |
Evaluation Exercise (AO3)
Task: Using the table above, write a short (8‑10 sentence) evaluation of the surveillance system used during the 2014‑16 Ebola outbreak, focusing on the five criteria and its impact on the response cycle.
Mark‑scheme hint: 1 mark for each criterion identified, 1 mark for a specific Ebola example, 1 mark for a balanced judgement (strength + weakness) – total 15 marks.
10. AO2 – Interpreting Geographical Data (Exam‑style Questions)
- 20 marks – A country’s health ministry releases data for a novel influenza strain (see table). Calculate incidence, prevalence and CFR for the first month. Using a line graph of weekly Rₜ values, comment on the effectiveness of the vaccination campaign introduced in week 3.
Skills: quantitative calculations (AO2); trend interpretation and evaluation of control measures (AO3).
- 15 marks – Compare how passive surveillance and event‑based surveillance would perform in detecting a cholera outbreak in (a) a densely populated river delta in Bangladesh, and (b) a sparsely populated high‑altitude region of Peru. Discuss scale, representativeness and challenges.
Skills: knowledge of surveillance types (AO1); application to contrasting physical/human contexts (AO2); balanced evaluation (AO3).
- 25 marks – Using the SIR model, outline how a 30 % reduction in the transmission rate (β) could be achieved through (i) water‑treatment and (ii) vector control. For each, discuss likely spatial variation in impact across urban, peri‑urban and rural zones, and evaluate socio‑economic barriers.
Skills: understanding of modelling (AO1); geographical application (AO2); critical evaluation (AO3).
11. Summary Checklist for Students (AO1‑AO3)
- 🔎 Define and calculate incidence, prevalence, CFR, R₀ and Rₜ (AO1).
- Distinguish passive, active, syndromic, laboratory‑based and event‑based surveillance; give a geographical example for each (AO1).
- Explain each stage of the response cycle and link it to GIS/remote‑sensing tools (AO2).
- Interpret hydrographs, climate diagrams and tectonic maps in the context of disease spread (AO2).
- Construct and manipulate a simple spatial SIR model; simulate at least two interventions (AO2).
- Evaluate a surveillance system using the five criteria, providing balanced judgments and real‑world examples (AO3).
- Practice the exam‑style questions above; check answers against the mark‑scheme hints.