global distribution and spatial and time variation of influenza (flu)

Cambridge A‑Level Geography (9696) – Global Themes

Topic 4: Disease and Geography – Case Study : Influenza (Flu)

Learning Objectives (Assessment Objectives)

• AO1 – Knowledge: Define key virological and epidemiological terms, describe the virus, and outline its global distribution and spatial‑temporal patterns.
• AO2 – Application & Analysis: Interpret maps, graphs and data sets; calculate epidemiological parameters (e.g., $R_0$); analyse causes of spatial and temporal variation.
• AO3 – Evaluation: Critically assess public‑health responses, socio‑economic inequality and climate‑change implications for future influenza risk using a structured evaluation framework.

Key Geographical Concepts Integrated in the Case Study

Glossary (AO1 – Core Terminology)

1. What Is Influenza? (AO1)

• Acute respiratory infection caused by influenza viruses (types A, B, C). Type A is epidemiologically most important because it infects birds, pigs and humans, enabling both antigenic shift and drift.
• Typical epidemiological parameters:
  • Basic reproduction number ($R_0$): 1.2–2.0 for seasonal flu; 2.5–3.5 (or higher) for pandemic strains.
  • Incubation period: 1–4 days.
  • Infectious period: up to 7 days (longer in children).

2. Global Distribution of Influenza (AO2)

Intensity varies with:

• Latitude (temperature & absolute humidity gradients).
• Human population density and connectivity (air‑travel hubs).
• Presence of animal reservoirs (wild waterfowl migration routes, pig farms).

3. Spatial Variation (AO2)

1. Temperate zones (30°–60° latitude): Sharp winter peaks (Dec–Feb NH, Jun–Aug SH) because low temperature and low absolute humidity increase viral survival in aerosols.
2. Tropical zones (within 30° latitude): Weak or bi‑annual seasonality; peaks often linked to rainy seasons when indoor crowding rises.
3. High‑altitude & urban centres: Higher population density and rapid transport accelerate spread; temperature inversions can trap aerosols.

4. Temporal Variation (AO2)

• Seasonal influenza: Predictable, roughly Gaussian winter peaks in temperate regions; flatter curves in the tropics.
• Pandemic influenza: Irregular, rapid global spread of a novel strain; higher $R_0$, often higher CFR.

5. Factors Controlling Spatial & Temporal Patterns (Cause‑and‑Effect, Systems)

• Climatic factors: Low temperature & absolute humidity → longer virus survival in droplets.
• Human mobility: ≈ 2 billion international passenger journeys per year; a virus can travel between continents within 24 h.
• Animal reservoirs: Wild waterfowl (natural hosts) and domestic poultry/pigs (mixing vessels) supply genetic material for shift events.
• Population immunity: Prior infection or vaccination reduces susceptibility; waning immunity fuels drift.
• Socio‑economic conditions: Overcrowding, limited health services and low vaccine uptake amplify outbreak magnitude.
• Climate change: Shifts in temperature and precipitation may expand zones of optimal transmission and alter bird migration routes.

6. Impacts of Influenza (AO2 & AO3)

7. Case Studies (AO1‑AO3)

7.1 2009 Swine Flu (H1N1pdm09)

• Originated in North America; reached all continents within six months.
• Estimated 1.4 billion infections; 151 000–575 000 deaths.
• Key drivers: pig‑human reassortment, modern air travel, early WHO pandemic alert.
• Evaluation: Effectiveness of WHO’s phased response; role of pre‑existing immunity in reducing mortality compared with 1918.

7.2 2017–2018 H7N9 Avian Influenza in China

• First human cases reported in 2013; a major wave in 2017‑18 with > 1 500 confirmed cases.
• Transmission linked to live‑bird markets; virus showed limited human‑to‑human spread but high CFR (~ 40 %).
• Control measures: market closures, poultry vaccination, enhanced surveillance.
• Evaluation: Success of rapid market‑closure policies versus economic loss for small‑scale farmers; equity issues in rural provinces.

7.3 Seasonal Influenza in East Asia (2003‑2005)

• High population density and frequent cross‑border travel (China‑Hong Kong‑Japan) produced overlapping epidemic peaks.
• National surveillance systems showed synchronised weekly ILI curves across the region.
• Evaluation: Importance of regional data sharing and coordinated vaccination campaigns; challenges of differing health‑system capacities.

7.4 Climate‑Change Influence – Recent H5N1 Human Cases (2022‑2024)

• Warmer winters in parts of Southeast Asia and Eastern Europe extended viral persistence in poultry markets.
• 2022‑2024 saw sporadic human infections in Russia, Mongolia and Vietnam, coinciding with altered migratory patterns of waterfowl.
• Evaluation: How projected temperature rises could increase the frequency of antigenic‑shift events and the need for integrated “One Health” surveillance.

8. Global Theme 2 – Climate Change and Health (Second Required Topic)

• Mechanism: Rising temperatures and altered precipitation modify absolute humidity, influencing aerosol stability; they also shift bird migration routes, affecting the geographic overlap of animal reservoirs and human populations.
• Geographical evidence: IPCC‑aligned modelling predicts a poleward shift of high‑risk zones by 2050, with increased seasonal intensity in higher latitudes.
• Policy relevance: Integration of influenza surveillance into climate‑adaptation plans (e.g., WHO “One Health” early‑warning systems).

9. Data‑Analysis Activities (AO2)

1. Graph interpretation: Using WHO FluNet data (provided), plot weekly ILI rates for London (temperate) and Nairobi (tropical). Identify peak weeks and calculate the amplitude of seasonal variation (peak – baseline).
2. R₀ calculation (early‑growth method): Given the epidemic curve for the 2009 pandemic in Mexico (sample data table below), calculate the exponential growth rate (r) and estimate $R_0 = 1 + r \times D$, where D = average infectious period (≈ 3 days).
3. Mapping exercise: Create a choropleth map of influenza vaccination coverage (2018‑2022) using the supplied data set. Analyse spatial inequalities and relate them to GDP per capita.
4. Systems diagram: Sketch a causal‑loop diagram linking climate variables, bird migration, viral survival, human travel and outbreak magnitude.

Sample Data Set – Weekly ILI Cases (Mexico, 2009 Pandemic)

10. Structured Evaluation Framework (AO3)

11. Evaluation Tasks (AO3)

• Debate the merits of universal annual vaccination versus risk‑group targeting in low‑resource settings using the evaluation framework above.
• Critically assess the WHO “Pandemic Influenza Preparedness Framework” – strengths, weaknesses and lessons learned from the 2009 pandemic and the COVID‑19 experience.
• Discuss how climate‑change mitigation (e.g., reducing deforestation, improving wet‑land management) could indirectly lower influenza pandemic risk.

12. Summary Table – Major Influenza Pandemics (CE 2000‑present)

13. Suggested Classroom Resources (Required Resource Types) – Linked to Activities

• Map (choropleth of global influenza incidence): Used in Activity 1 (global distribution discussion) and Activity 3 (vaccination‑coverage mapping).
• Graph (seasonal ILI curves for London & Singapore): Supports Activity 1 (graph interpretation) and illustrates spatial‑temporal variation.
• Photograph (1918‑pandemic public‑health poster – for historical contrast): Provides primary‑source analysis in the evaluation of public‑health messaging.
• Data table (weekly ILI cases for Mexico 2009): Basis for Activity 2 (R₀ calculation).
• Extract (WHO “Pandemic Influenza Preparedness Framework” executive summary): Central to Evaluation Task 2 (AO3).
• Satellite‑derived climate data (temperature & humidity maps): Used in Activity 4 (systems diagram) to link climate variables with viral survival.

14. Key Take‑aways

• Influenza is a globally distributed disease; spatial patterns are shaped by climate, human mobility, animal reservoirs and socio‑economic factors.
• Seasonal influenza shows predictable latitudinal cycles, whereas pandemics arise from antigenic shift and spread rapidly via modern transport networks.
• Understanding these patterns is essential for designing effective public‑health responses, vaccination strategies and for anticipating how climate change may modify future risk.